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# Application of Phonocardiogram and Electrocardiogram Signal Features in Cardiovascular Abnormality Recognition

R. Geetha Ramani, Abhinand Ganesh, Roshni Balasubramanian(✉), and Aruna Srikamakshi Ramkumar

Department of Information Science and Technology, CEG, Anna University, Chennai
600 025, India

roshnibala.official@gmail.com

**Abstract.** Globally, cardiovascular disorders are the leading cause of death. The current situation calls for a precise method to determine whether a person's heartbeat and heart signals are irregular. Electrocardiograms (ECG) and phonocardiograms (PCG) are techniques to identify cardiovascular abnormalities in modern medicine. As the first step in the diagnosis, sound waves from heartbeats could be classified as normal or abnormal. In order to predict the presence of cardiovascular abnormalities, our study compares the use of ECG, PCG, and a hybrid feature approach. The PhysioNet/CinC 2016 and PhysioNet/CinC 2017 datasets were used. With the AdaBoost ensemble classifier using Random Forest as the base model, it has been found that the PCGwith-ECG hybrid feature strategy outperforms the former methodologies in terms of sensitivity with a score of 95.16

**Keywords:** Heart Signals · Machine Learning · Feature Extraction

## 1 Introduction

The heart's proper functioning is crucial to avoid repercussions in one's health. Heart signals and heart beats can help in indicating issues which may arise in the event of acceleration or deceleration of blood flow and abnormalities in heart valves, which may produce murmurs that are not easily audible to the human ear. Cardiac Auscultation involves listening for and characterizing heart murmurs, usually done with a stethoscope. Presently, highly skilled and experienced physicians and cardiologists have the ability to classify normal and abnormal heart sounds and diagnose patients with heart diseases. Only 20-40% accuracy in detection of abnormalities may be obtained by the primary physicians and around 80% of accuracy by expert specialists. [1]. This requires an AI-based system that can achieve this task efficiently.
The leading method in heart arrhythmia is electrocardiogram (ECG), which measures the electrical activity generated by the heart as it contracts. The Phonocardiogram (PCG), which plots heart sounds and murmurs, is another tool for identifying irregular heartbeats. However, a diagnosis based solely on an ECG signal or PCG signal independently cannot identify all cases of heart abnormality and symptoms of cardiovascular irregularities. Both of the aforementioned signals play crucial roles in the detection in its unique ways. An ECG signal might show a variety of normal and abnormal heart behaviours that can be observed through the resulting signals. However, other symptoms, including heart murmurs, which are frequently brought on by faulty heart valves, cannot be identified through only the electrocardiogram. [2] Consequently, integrating the extracted features from the phonocardiograms and electrocardiograms can result in highly effective diagnostics of cardiovascular abnormalities.

In this research, we have made use of pre-processing techniques to the publicly available PhysioNet/CinC 2016 PCG and PhysioNet/CinC 2017 ECG dataset to compare the results. We then opt for a hybrid PCG-ECG technique to verify if combining both the types of medical data yield a better result. We then use various feature selection techniques and classification algorithms to study the methodologies that deliver the highest accuracy, sensitivity and specificity as metrics.

# 2 Related Work

## 2.1 Heart Signal Classification and usage of the PhysioNet/CinC Dataset

In order to classify heart signals, there are two main paths that are majorly followed: extracting features directly from the signal, or making use of spectrograms to classify the signal via image features. The traditional image processing method has been a popular topic of research. For classification of PCG signals, it has involved conversion of the signals to their Mel-Frequency Spectrograms.

[3] has conducted a literature survey of all the datasets available currently for heart signal classification. They also made use of the CirCor DigiScope dataset for the study. Automatic segmentation of the heart audio samples had been done using three algorithms - Hidden Semi Markov Model, Logistic Regression-HSMM and Deep-CNN. However, the absence of frequency and time based features in their study for classification purposes, however, was its principal drawback. [4] used the PhysioNet/CinC database in order to classify heart sound signals. The authors made use of a mobile application along with a digital stethoscope in order to perform detection of heartbeat sounds. They achieved an accuracy of 93% to classify the normal and abnormal heart sound signals. However, the major drawback associated with this study was the lack of transparency in terms of the extent of algorithms used for each step. [5] proposed a modification to the Hidden Semi Markov Model by adding a Logistic Regression component to it in order to perform more accurate automatic segmentation on heart signal data. They also implemented the Viterbi algorithm in order to determine the most likely
sequence of states. However, the major drawback associated with this study is the size of the dataset, i.e., only 405 recordings were chosen from a small set of population.

[1] made use of both PASCAL and the PhysioNet dataset in order to classify phonocardiogram signals into the categories of normal and abnormal. To conduct classification, the traditional image processing methodology was followed after segmenting the heart signals. The signals were first filtered from background noise using an IIR filtering technique after which the Mel-frequency spectrograms were generated. In order to select the right segments via clustering, a Mixture Gaussian Model was used. They achieved an overall classification accuracy of 97% via the image processing approach for the PhysioNet/CinC dataset, and 87% for the PASCAL dataset. [6] conducted a review of all the submissions for the PhysioNet/CinC 2016 challenge and compiled the scores for all the top submissions to the challenge. The top submission had a sensitivity score of 94%. However, the main drawback associated with this dataset was the lack of features that focused on the age group, gender, etc. [7] made use of the 2016 challenge data from PhysioNet/CinC, rather than the updated dataset. They used and compared multiple classification algorithms, including, K-Nearest Neighbor, Decision Tree, Ensemble Classifier, Artificial Neural Network and Long-Short-Term Memory. Different feature selection algorithms were also employed and the results for the same were discussed.

## 2.2 Contributions

The failure to explore hybrid feature strategies in order to demonstrate a model improvement has been seen as a flaw in all the studies examined. The papers solely followed either the ECG or PCG track. Our research intends to address this issue and provide a more accurate model that exhibits a general improvement. Additionally, the majority of the existing studies conducted exclusively employ the spectrogram technique for image processing. When thousands of heart signals are taken into account, this can become a costly task. In our study, we investigate the feasibility of directly extracting features from the signal and using them for categorization.

# 3 Signals in Medical Domain Data

## 3.1 PCG Signals

The first test conducted by a physician to assess a patient's health is via cardiac auscultation. The signal produced by virtue of cardiac auscultation is referred to as phonocardiogram (PCG). It has been recognised by researchers that phonocardiogram signals are not widespread with respect to heart sound analysis [8]. The phonocardiogram consists of at least two vibrations - S1 and S2 which are produced by the closing of the atrioventricular valves (at the beginning of systole) and by the closing of the aortic and pulmonary valves (at the end of
systole). The phonocardiogram plots the heart sounds over a time period and is captured through digital stethoscopes.

The PCG offers data such as time, frequency, quality, tone and location of different cardiac sounds. The stethoscope is placed at specific locations to identify the auscultation of specific heart valves [3].

<table><caption>**Table 1.** Auscultation of Valves and Specific Locations</caption><thead><tr><td>No.</td><td>Valve</td><td>Location</td></tr></thead><tbody><tr><td>1</td><td>Aortic Valve</td><td>2nd Intercostal Space, Right Sternal Border</td></tr><tr><td>2</td><td>Pulmonic Valve</td><td>2nd Intercostal Space, Left Sternal Border</td></tr><tr><td>3</td><td>Tricuspid Valve</td><td>Left Lower Sternal Border</td></tr><tr><td>4</td><td>Mitral Valve</td><td>5th Intercostal Space, Mid-clavicular line</td></tr></tbody></table>

The nature of such vibrations can contribute greatly to the recognition of cardiac abnormality. For example, mitral regurgitation can be noticed through high pitched holosystic murmurs, identified in the mitral valve. Similarly, health-care professionals identify various heart abnormalities via the heart beat sounds and strong/weakness of the valves closing/opening auscultations.

## 3.2 ECG Signals

An electrocardiogram (ECG) captures the electrical signals that are generated from the heart in order to check various heart conditions. ECGs from healthy hearts have a characteristic shape. Any irregularity in the heart rhythm or damage to the heart muscle can change the electrical activity of the heart so that the shape of the ECG is changed. In ECG the electrodes are placed in various points of the chest, arms and legs and are connected to the ECG machine using lead wires. The cost of obtaining an ECG is $32 - $54 for insured patients, whereas PCGs are cost-effective techniques- only requiring a digital stethoscope and a professional to analyse the sounds [1].

A normal ECG reading has four parts- P wave, QRS complex, T wave and U wave. Each of these parts represents a unique state of the cardiac cycle as listed in Table 2.

<table><caption>**Table 2.** Waves of ECG and Electrical Activity</caption><thead><tr><td>No.</td><td>Entity</td><td>Electrical Activity</td></tr></thead><tbody><tr><td>1</td><td>P wave</td><td>Depolarization of atria</td></tr><tr><td>2</td><td>QRS Complex</td><td>Depolarization of ventricles</td></tr><tr><td>3</td><td>ST segment</td><td>0 potential b/w ventricular depolarization repolarization</td></tr><tr><td>4</td><td>T wave</td><td>Repolarization of ventricles</td></tr></tbody></table>
An alteration of the normal ECG reading to an elongated or shortened wave, segment or complex indicates abnormalities. For example, an elevation of ST segment indicates left ventricular aneurysm. Hence, healthcare professionals look for any abnormalities from the normal ECG to determine any types of abnormalities in the human heart.

# 4 Proposed Methodology

For this research, the PhysioNet/CinC datasets are made use of. The 2016 dataset involves a separate training set that consists of both PCG and ECG samples for the same patients and the 2017 dataset deals with purely ECG samples. The datasets are further explicated in Table 3. The labels for PhysioNet/CinC 2016 were normal or abnormal, whereas, for PhysioNet/CinC 2017 class labels of normal, atrial fibrillation (AF) and other rhythms were provided. Further, there were nearly 200 records without any class labels.

<table><caption>Table 3. Dataset Analysis</caption><thead><tr><td>Dataset</td><td>File Type</td><td>Training Samples</td><td>Signal</td><td>Labels</td></tr></thead><tbody><tr><td>PhysioNet/CinC-2016</td><td>.wav</td><td>3240</td><td>PCG</td><td>Normal Abnormal</td></tr><tr><td>PhysioNet/CinC-2016</td><td>.dat</td><td>409</td><td>ECG</td><td>Normal Abnormal</td></tr><tr><td>PhysioNet/CinC-2017</td><td>.mat</td><td>8528</td><td>ECG</td><td>Normal Abnormal(AF)</td></tr></tbody></table>

## 4.1 PCG Signals

For the experiments conducted by this research, 49 features have been identified from such PCG signals. These features are of either time or frequency domain and are given in Table 4.

Among the features extracted, the MFCC features are usually aimed at detected phones in speech. This can be used to distinguish heart sounds as well. Since a given heartbeat sound recording can contain many such phones, the entire duration of the sound is divided into multiple segments; and 39 features are extracted from each segment.

In order to extract the MFCC features, the heartbeat sound is first converted into multiple windows. Discrete Fourier Transformation is applied completely to convert the signal from the time domain to the frequency-domain, which helps in the extraction of the Mel-frequency cepstral coefficients. Further, the Mel() function is applied to the frequencies obtained from DFT in accordance with the mel filter bank. A logarithm function is applied, after which the IDFT (Inverse
<figure><img src="image_2.png" /><figcaption>Fig. 1. Heart Signal Processing Architecture</figcaption></figure>

Discrete Fourier Transformation) is applied to the values obtained. From each window, 39 unique MFCC features are obtained. In order to normalize it for the entirety of the particular signal, the mean is calculated for each feature throughout the windows to arrive at only 39 features finally for a particular PCG signal.

However, a total of 49 features can be a lot - possibly contributing to the curse of dimensionality. Thus, feature selection algorithms are used in order to identify the most ranked features which are finally used for classification.

## 4.2 ECG Signals

For the experiments conducted for ECG signals, 86 features were used. These features are of morphological significance, taking into account the R-R intervals of each cardiac cycle. Sebastian D Goodfellow et. Al [9] used a methodology to extract the various morphological features, which was adopted and modified in order to obtain the 86 feature set. The features are of 2 domains - full waveform features and RR- Interval features. For full waveform features, the amplitude features of the waveforms were calculated. These waveforms include - Min, Max, Mean, Median, Standard Deviation, Skew and Kurtosis

The full waveform features were then generated from stationary wavelet transform decomposition. Here, the levels of decomposition of 1 to 4 were calculated. The spectral density ratio of each was stored in three different levels - high, medium and low. The log entropy for the levels was extracted as well
<table><caption>**Table 4.** PCG Wavelet Features</caption><thead><tr><td>#</td><td>Feature</td></tr></thead><tbody><tr><td>1-39</td><td>MFCCs (Mel Frequency Cepstral Coefficients)</td></tr><tr><td>40</td><td>Occupied Bandwidth</td></tr><tr><td>41</td><td>Zero Cross Rate</td></tr><tr><td>42</td><td>Peak Magnitude to RMS Ratio</td></tr><tr><td>43</td><td>Maximum to Minimum Difference</td></tr><tr><td>44</td><td>Median Frequency</td></tr><tr><td>45</td><td>Power Bandwidth</td></tr><tr><td>46</td><td>Equivalent Noise Bandwidth</td></tr><tr><td>47</td><td>Mean Frequency</td></tr><tr><td>48</td><td>Band Power</td></tr><tr><td>49</td><td>Root Sum of Squares</td></tr></tbody></table>

as Higuchi's fractal dimension. These features totally account for 40 of the 86 features extracted from the ECG signals [9].

Apart from the aforementioned features, the RR-I features include the amplitude min, max, mean, median, standard deviation, root mean square, skew and kurtosis. These features have been calculated for $RRI_i$ and $RRI_{i+1}$. Heart variability features such as the beat-to-beat intervals produce the pNN10, pNN20, pNN50 etc. Here, pNN10 refers to the proportion of successive pairs of NNs that differ more than 10 ms, divided by the total number of NNs.

<table><caption>**Table 5.** Full Waveform Features</caption><thead><tr><td>Stationary Wavelet</td><td>Transform Decomposition</td><td>Coefficients</td></tr></thead><tbody><tr><td>Level 1</td><td></td><td>Approximation Coefficient</td></tr><tr><td>Level 2</td><td></td><td>Detail Coefficient</td></tr><tr><td>Level 3</td><td></td><td></td></tr><tr><td>Level 4</td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 6.** Full Waveform Features: Power Spectral Density Ratios & Additional Features</caption><thead><tr><td>Spectral Density</td><td>Frequency Band</td></tr></thead><tbody><tr><td>Low</td><td>03 - 10 Hz</td></tr><tr><td>Med</td><td>10 - 30 Hz</td></tr><tr><td>High</td><td>30 - 45 Hz</td></tr><tr><td>Log Entropy</td><td></td></tr><tr><td>Higuchi Fractal Dimensions</td><td></td></tr></tbody></table>
<table><caption>**Table 7.** R-R Interval Features</caption><thead><tr><th></th><th>pNN01</th><th>pNN50</th><th>pNN100</th></tr></thead><tbody><tr><th>Max</th><td>pNN10</td><td>pNN60</td><td>pNN200</td></tr><tr><th>Min</th><td>pNN10</td><td>pNN60</td><td>pNN200</td></tr><tr><th>Median</th><td>pNN20</td><td>pNN70</td><td>pNN400</td></tr><tr><th>Mean</th><td>pNN30</td><td>pNN80</td><td>pNN600</td></tr><tr><th>Standard Deviation</th><td>pNN0</td><td>pNN90</td><td>pNN800</td></tr></tbody></table>

The levels of decomposition listed in Table 5, with various combinations of the approximation and detail coefficients along with the spectral densities, log entropy and Higuchi Fractal Dimensions given in Table 6. These combinations give the full waveform features. The RRI features are listed in Table 7. These features are taken for intervals between various R peaks of the ECG signal.

## 4.3 Hybrid Feature Approach

The hybrid feature approach involves combining both the PCG and ECG features for the same subject in the study. In this experiment, the hybrid feature matrix consists of a total of 137 features. The study aims to improve the accuracy of cardiac abnormality diagnosis by using an extensive feature matrix.

# 5 Performance of the Models

The models are evaluated using a variety of parameters, but Accuracy, Sensitivity and Specificity are the primary evaluation metrics used. In papers like [10], the Specificity and Sensitivity scores dip down even when there's high accuracy. This major disadvantage has been mitigated in this research. The formulae used to calculate such metrics are as follows -

$$Accuracy = \frac{TP + TN}{TP + FP + TN + FN} \qquad (1)$$

$$Sensitivity = \frac{TP}{TP + FN} \qquad (2)$$

$$Specificity = \frac{TN}{TN + FP} \qquad (3)$$

Performance of models on PhysioNet/CinC-2016 dataset when the 'training' subset is considered, which has the ECG and PCG recordings for the same patient are provided in Table 8. In Table 8, the feature selection techniques - Kruskal Wallis and ReliefF algorithms have been used in some experiments. The ReliefF algorithm was used with a feature importance threshold value of 0.001. For Kruskal Wallis, the feature importance threshold was set to 15.00. It can be
observed that when both PCG and ECG recordings are considered for the same patient, the hybrid feature matrix provides an accuracy of 91.37% and sensitivity of 95.16%. When only ECG features were considered from the PhysioNet/CinC 2016 dataset, a maximum accuracy of 87.16% was observed and the same was 88.11% for the PhysioNet/CinC 2017 dataset. It is observed that the hybrid feature approach outperforms the rest by increasing the accuracy by 8–16%. From certain experiments, it can also be inferred that the accuracy does not necessarily increase when feature selection approaches are incorporated into the sets. After further experiments on using only ECG features for the same “training-a” subset of the PhysioNet/CinC-2016 dataset. On trying with both raw feature values and min-max normalization, better results were not obtained and the metrics did not improve by a large margin. Upon considering the possibility of low performance of the models owing to the lack of an extensive dataset, similar experiments have been performed on the PhysioNet-2017 Challenge dataset, which contains a total of 8244 ECG samples.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Accuracy verses Number of Axes/Iterations for the Normalized ECG-PCG Hybrid Set</figcaption></figure>

Principal Component Analysis is a dimensionality reduction technique that is used to mitigate overfitting. However, PCA can also be used to introduce new features in order to enhance the accuracies of pre-existing models. This has been made use of in all three feature models - PCG, ECG and PCG-ECG hybrid. It has been observed that the accuracy increases to 91.37% from a maximum of 86.1% for the hybrid matrix when 94 Components of PCA were taken. This has been depicted in Fig. 2, where the accuracy greatly improves when the PCA features are taken into account. Figure 2 compares the results between the PCA axes data, the extracted feature data and the accuracy of when both PCA axes and extracted features data are combined.
<table><caption>**Table 8.** Performance of Models on “training-a” of PhysioNet/CinC 2016</caption><thead><tr><th>Data Features</th><th>#Feature</th><th>Factorial Analysis</th><th>Feature Selection</th><th>Classifier</th><th>Accuracy</th><th>Specificity</th><th>Sensitivity</th></tr></thead><tbody><tr><td>ECG only</td><td>41</td><td>-</td><td>-</td><td>AdaBoost Ensemble (Decision Tree Classifier Base model)</td><td>63.70%</td><td>65.90%</td><td>20%</td></tr><tr><td>PCG-ECG Hybrid</td><td>23</td><td>-</td><td>Kruskal Wallis</td><td>Decision Tree Classifier</td><td>73.10%</td><td>91.0%</td><td>29.10%</td></tr><tr><td>PCG only</td><td>23</td><td>-</td><td>Kruskal Wallis</td><td>Decision Tree Classifier</td><td>73.90%</td><td>77.30%</td><td>56.40%</td></tr><tr><td>PCG only</td><td>51</td><td>-</td><td>-</td><td>K-Nearest Neighbor</td><td>76.20%</td><td>76.60%</td><td>46.30%</td></tr><tr><td>PCG-ECG Hybrid</td><td>137</td><td>-</td><td>-</td><td>Decision Tree Classifier</td><td>76.50%</td><td>88.9%</td><td>46.20%</td></tr><tr><td>PCG-ECG Hybrid</td><td>44</td><td>-</td><td>ReliefF Algorithm</td><td>Decision Tree Classifier</td><td>76.50%</td><td>85.8%</td><td>53.80%</td></tr><tr><td>PCG-ECG Hybrid</td><td>90</td><td>-</td><td>ReliefF Algorithm</td><td>Artificial Neural Network</td><td>81.90%</td><td>86.7%</td><td>78.20%</td></tr><tr><td>PCG-ECG Hybrid</td><td>137</td><td>-</td><td>-</td><td>Cubic Support Vector Machine</td><td>86.10%</td><td>78.5%</td><td>93.80%</td></tr><tr><td>PCG-ECG Hybrid</td><td>137</td><td>-</td><td>-</td><td>AdaBoost Ensemble (Decision Tree Classifier Base model)</td><td>86.10%</td><td>82.3%</td><td>89.40%</td></tr><tr><td>PCG-ECG Hybrid</td><td>135</td><td>-</td><td>ReliefF Algorithm</td><td>Cubic Support Vector Machine</td><td>86.60%</td><td>79.2%</td><td>94.10%</td></tr><tr><td>PCG only</td><td>15</td><td>PCA (15 axis)</td><td>-</td><td>AdaBoost Ensemble (Decision Tree Classifier Base model)</td><td>86.99%</td><td>64.76%</td><td>92.73%</td></tr><tr><td>PCG-ECG Hybrid</td><td>93</td><td>PCA (93 axes)</td><td>-</td><td>AdaBoost Ensemble with Random Forest Classifier</td><td>91.37%</td><td>87.03%</td><td>95.16%</td></tr></tbody></table>
<table><caption>**Table 9.** PhysioNet/CinC 2016 - ECG Data (Training-A): Experiments Conducted on Normalized Data for Random Forest Classifier</caption><thead><tr><th>Feature Selection</th><th>Factorial Analysis</th><th>Assessment</th><th>Accuracy</th><th>Specificity</th><th>Sensitivity</th></tr></thead><tbody><tr><td>Fisher Filtering (x-best = 65)</td><td>-</td><td>Train Test (70-30)</td><td>55.74%</td><td>63.53%</td><td>37.84%</td></tr><tr><td>Fisher Filtering (x-best = 65)</td><td>PCA (17 axes)</td><td>Cross Validation (3 fold)</td><td>61.73%</td><td>72.22%</td><td>35.90%</td></tr><tr><td>Fisher Filtering (x-best = 65)</td><td>PCA (17 axes & all extracted features)</td><td>Cross Validation (3 fold)</td><td>62.96%</td><td>76.04%</td><td>30.77%</td></tr><tr><td>Fisher Filtering (x-best = 65)</td><td>PCA (65 axes & all extracted features)</td><td>Cross Validation (3 fold)</td><td>63.70%</td><td>69.10%</td><td>50.43%</td></tr><tr><td>Fisher Filtering (x-best = 45)</td><td>-</td><td>Cross Validation (3 Folds)</td><td>63.95%</td><td>73.26%</td><td>41.03%</td></tr><tr><td>Fisher Filtering (x-best = 65)</td><td>-</td><td>Cross Validation (3 Folds)</td><td>65.19%</td><td>74.31%</td><td>42.74%</td></tr><tr><td>Fisher Filtering (x-best = 55)</td><td>PCA (Only 15 axes)</td><td>Cross Validation (3 fold)</td><td>65.19%</td><td>77.08%</td><td>35.90%</td></tr><tr><td>-</td><td>-</td><td>Cross Validation (3 Folds)</td><td>66.42%</td><td>73.96%</td><td>47.86%</td></tr><tr><td>Fisher Filtering (x-best = 55)</td><td>PCA (15 axes & all extracted features)</td><td>Cross Validation (3 fold)</td><td>68.64%</td><td>75.35%</td><td>52.14%</td></tr><tr><td>-</td><td>PCA (21 axes)</td><td>Cross Validation (3 fold)</td><td>86.42%</td><td>89.24%</td><td>79.49%</td></tr><tr><td>-</td><td>PCA (21 axes & all extracted features)</td><td>Cross Validation (3 fold)</td><td>87.16%</td><td>89.58%</td><td>81.20%</td></tr></tbody></table>
<table><caption>**Table 10.** PhysioNet/CinC 2017 - ECG Data: Experiments Conducted on Data before Normalization</caption><thead><tr><th>Feature Selection</th><th>Factorial Analysis</th><th>Classifier</th><th>Assessment</th><th>Accuracy</th><th>Specificity</th><th>Sensitivity</th></tr></thead><tbody><tr><td>-</td><td>PCA (16 axes)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>73.13%</td><td>77.43%</td><td>66.34%</td></tr><tr><td>-</td><td>PCA (All PCA axes all extracted features)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>78.00%</td><td>81.96%</td><td>71.73%</td></tr><tr><td>Fisher Filtering (p-value = 0.001)</td><td>PCA (14 axis only all extracted features)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>78.60%</td><td>82.36%</td><td>72.67%</td></tr><tr><td>-</td><td>PCA (14 axes)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>78.89%</td><td>82.67%</td><td>72.92%</td></tr><tr><td>Fisher Filtering (p-value = 0.001)</td><td>-</td><td>Random Forest Classifier</td><td>Cross Validation (3 Folds)</td><td>78.89%</td><td>82.67%</td><td>72.92%</td></tr><tr><td>Fisher Filtering (p-value = 0.001)</td><td>-</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>79.25%</td><td>83.52%</td><td>72.48%</td></tr><tr><td>-</td><td>PCA (16 axes)</td><td>Random Forest Classifier (Boosting)</td><td>Cross Validation (3 folds)</td><td>79.26%</td><td>85.19%</td><td>69.88%</td></tr><tr><td>-</td><td>PCA (16 axes all extracted features)</td><td>Random Forest Classifier (Boosting)</td><td>Train-Test (75-25)</td><td>87.86%</td><td>91.60%</td><td>81.82%</td></tr></tbody></table>
<table><caption>**Table 11.** PhysioNet/CinC 2017 - ECG Data: Experiments Conducted on Normalized Data</caption><thead><tr><th>Feature Selection</th><th>Factorial Analysis</th><th>Classifier</th><th>Assessment</th><th>Accuracy</th><th>Specificity</th><th>Sensitivity</th></tr></thead><tbody><tr><td>-</td><td>PCA (16 axes)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>73.13%</td><td>77.43%</td><td>66.34%</td></tr><tr><td>-</td><td>PCA (16 axes all extracted features)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>77.97%</td><td>81.82%</td><td>71.88%</td></tr><tr><td>Fisher Filtering (p-value = 0.001)</td><td>PCA (14 axis only &amp; all extracted features)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>78.30%</td><td>81.92%</td><td>72.57%</td></tr><tr><td>-</td><td>PCA (14 axes)</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>78.63%</td><td>82.51%</td><td>72.48%</td></tr><tr><td>Fisher Filtering (p-value = 0.001)</td><td>-</td><td>Random Forest Classifier</td><td>Cross Validation (3 Folds)</td><td>78.76%</td><td>82.55%</td><td>72.76%</td></tr><tr><td>Fisher Filtering (p-value = 0.001)</td><td>-</td><td>Random Forest Classifier</td><td>Cross Validation (3 fold)</td><td>79.46%</td><td>83.60%</td><td>72.92%</td></tr><tr><td>-</td><td>PCA (16 axes)</td><td>Random Forest Classifier (Boosting)</td><td>Cross Validation (3 folds)</td><td>80.08%</td><td>86.34%</td><td>70.19%</td></tr><tr><td>-</td><td>PCA (16 axes all extracted features)</td><td>Random Forest Classifier (Boosting)</td><td>Train-Test (75-25)</td><td>88.11%</td><td>91.67%</td><td>82.33%</td></tr></tbody></table>

<table><caption>**Table 12.** Performance of Models on PhysioNet/CinC 2016 for PCG</caption><thead><tr><th>Methodology</th><th>Feature Selection</th><th>Classification</th><th>Accuracy</th><th>Sensitivity</th><th>Specificity</th></tr></thead><tbody><tr><td>36 Features</td><td>Relief</td><td>AdaBoost with Random Forest</td><td>92%</td><td>89.2%</td><td>92.8%</td></tr><tr><td>Z-Score Normalization + PCA components added (20)</td><td>-</td><td>AdaBoost with Random Forest</td><td>93.2%</td><td>93.6%</td><td>91.2%</td></tr></tbody></table>

## 6 Result and Conclusion

Through our research, the best results obtained were from the ECG-PCG hybrid data with Principal Component Analysis as well as a pure-PCG feature model with 20 PCA components added to it. A high specificity of 87.03% and sensitivity of 95.16% was noted in the hybrid approach. In [1], a maximum sensitivity of 94.6% was achieved using the PhysioNet/CinC 2016 dataset. However, our hybrid model enhances the sensitivity score.
It was also noted that the performance did not vary much between the nor-
malised and unnormalised data. Although the costs of obtaining both a PCG
and ECG reading are high, the approach towards a better diagnosis could help
identify abnormalities well in advance, thus preventing the loss of lives.

7 Future Scope

The research can be further improved by extracting image features from the
PCG and ECG signals after converting them to their Mel Spectrum forms and
comparing the results of classification with the current results. New features can
be identified using iterative principal component analysis and used as input for
the models. Further, deep neural networks can be used to enhance the specificity
and sensitivity scores.

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