Patent Description:
The present invention, in some embodiments thereof, relates to diagnosing neuro-psychiatric diseases and, more specifically, but not exclusively, to systems for diagnosing neuro-psychiatric diseases using EEG data.

Disorders of the brain like schizophrenia, epilepsy, depression, dementia and other mental and neurological disorders constitute about <NUM>% of the global disease burden (disability-adjusted life-years, DALYs) surpassing cardiovascular diseases and cancer combined (Kitchen Andren, Gabel, Stelmokas, Rich, & Bieliauskas, <NUM>, incorporated herein by reference in its entirety). As the general population ages, the burden of brain disorders increases (EBC, <NUM>; Feigin et al. , <NUM>, incorporated herein by reference in its entirety) however many brain diseases show an early-onset in life and, due to their chronic course, have an enormous health and socio-economic impact (Andersson et al. , <NUM>; Erkkinen, Kim, & Geschwind, <NUM>; Jennum, Gyllenborg, & Kjellberg, <NUM>; Livingston et al. , <NUM>; Wittchen et al. , <NUM>, incorporated herein by reference in its entirety). The consequences extend well beyond the healthcare system: loss of healthy life years and quality of life, burdens on health and social welfare systems, implications for labor markets with prolonged impairment, great dependency and significant reduced productivity. The absence of cures and the lack of pre-symptomatic diagnosis and possible preventive interventions makes disorders of the brain the greatest global challenge for health and social care in the 21st century (Malhi & Mann, <NUM>; Owen, Sawa, & Mortensen, <NUM>, incorporated herein by reference in its entirety).

According to a first aspect, not according to the invention, a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

According to a second aspect, a system for diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprises: at least one hardware processor executing a code for: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, and extracting a quantum potential value from p-adic representation of the plurality of clusters, wherein the medical state associated with the neuro-psychiatric disorder is diagnosed according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

According to a third aspect, a computer program for diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising program instructions which, when executed by a processor, cause the processor to perform: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, and extracting a quantum potential value from p-adic representation of the plurality of clusters, wherein the medical state associated with the neuro-psychiatric disorder is diagnosed according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

In a further implementation form of the first, second, and third aspects, further comprising computing a plurality of probability distribution functions each computed for a respective EEG dataset, wherein clustering comprises clustering the plurality of probability distribution functions, wherein the p-adic representation is computed based on clusters of the probability distribution functions.

In a further implementation form of the first, second, and third aspects, further comprising computing a plurality of similarity values, each respective similarity value indicative of similarity between a respective pair of the plurality of probability distribution functions, wherein clustering comprises computing a hierarchical relationship between the pairs of distribution functions according to the plurality of similarity values, wherein the p-acid representation is computed according to the hierarchical relationship.

In a further implementation form of the first, second, and third aspects, each of the plurality of similarity values comprises a Hellinger distance.

In a further implementation form of the first, second, and third aspects, the hierarchical relationship comprises a dendrogram, and wherein the p-acid representation is computed for each respective route through the dendrogram terminating at an edge corresponding to a certain EEG electrode.

In a further implementation form of the first, second, and third aspects, computing the p-adic representation comprises computing a plurality of p-adic representations each corresponding to one EEG electrode, and wherein extracting the quantum potential value comprises computing a single quantum potential value from the plurality of p-adic representations.

In a further implementation form of the first, second, and third aspects, further comprising: converting the p-adic representation into a rational number, wherein each of a plurality of p-adic representations is converted into a corresponding respective rational number, computing a probability distribution function from the rational number, wherein each of a plurality of probability distribution functions is computed from a corresponding rational number, wherein the quantum potential value is computed from the probability distribution function, wherein each of a plurality of quantum potential values is computed from the corresponding respective probability distribution function.

In a further implementation form of the first, second, and third aspects, each of the plurality of quantum potential values corresponds to one of the plurality of EEG electrodes, and further comprising computing an aggregation of the plurality of quantum potential values to generate an aggregated quantum potential value, wherein the diagnosing the medical state is according to the aggregated quantum potential value.

In a further implementation form of the first, second, and third aspects, aggregating comprises computing a mean of the plurality of quantum potential values corresponding to the plurality of EEG electrodes.

In a further implementation form of the first, second, and third aspects, the extracting a quantum potential value comprises extracting a time series including a plurality of quantum potential values each from a corresponding time interval of a plurality of time intervals during which the EEG datasets are obtained, and further comprising: computing a quantum potential mean and variability score (qpmvs) by aggregating the plurality of quantum potential values of the time series, wherein the diagnosing the medical state is according to the aggregated quantum potential value.

In a further implementation form of the first, second, and third aspects, the threshold that separates between presence of the medical state and non-presence of the medical state is set by: computing a plurality of quantum potential values, each quantum potential value for one of a plurality of subjects associated with an indication of the medical state or an indication of non-presence of the medical state, using respective EEG datasets, and setting the threshold to separate between quantum potential values of subjects associated with the indication of the medical state, and quantum potential values of subjects associated with the indication of non-presence of the medical state.

In a further implementation form of the first, second, and third aspects, p-adic comprises <NUM>-adic.

In a further implementation form of the first, second, and third aspects, further comprising treating the patient using a treatment effective for the medical state.

In a further implementation form of the first, second, and third aspects, the medical state is selected from the group consisting of: depression, schizophrenia, Alzheimer's disease (AD), and mild cognitive impairment (MCI), and the non-presence of the medical state is selected from the group consisting of: no neuro-psychiatric disorder, and another neuro-psychiatric disorder that is different from the medical state.

In a further implementation form of the first, second, and third aspects, the medical state is selected from the group consisting of: stable AD, stable MCI and the non-presence of the medical state is selected from the group consisting of: deteriorating AD, and deteriorating MCI.

In a further implementation form of the first, second, and third aspects, the medical state comprises a prediction of likelihood of developing the neuro-psychiatric disorder in the future, and the non-presence of the medical state comprises a prediction of likelihood of not developing the neuro-psychiatric disorder in the future.

In a further implementation form of the first, second, and third aspects, the medical state comprises a prediction of likelihood of positively clinically significantly responding to a certain treatment for the neuro-psychiatric disorder, and the non-presence of the medical state comprises a prediction of likelihood of no clinically significant response to the certain treatment for the neuro-psychiatric disorder.

In a further implementation form of the first, second, and third aspects, further comprising: computing a first quantum potential value for the subject prior to administration of a certain treatment for the neuro-psychiatric disorder, administering the certain treatment to the subject, computing a second quantum potential value for the subject after the administration of the certain treatment for the neuro-psychiatric disorder, and determining a clinically significant response to the certain treatment when the second quantum potential value is statistically significantly different from the first quantum potential value.

Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments, exemplary methods and/or materials are described below.

Some embodiments are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments may be practiced.

The present disclosure relates to diagnosing neuro-psychiatric diseases, predicting disease time course, evaluating treatment outcome and, more specifically, but not exclusively, to systems and methods for diagnosing neuro-psychiatric diseases using EEG data.

An aspect of some embodiments relate to systems, methods, an apparatus, and/or code instructions (stored in a memory and executable by one or more hardware processors) for computing a quantum potential value based on EEG datasets sensed by EEG electrodes sensing a head of the subject. The quantum potential value may be used for diagnosing a medical state of the subject, optionally a medical state associated with neuro-psychiatric disorder, by comparing the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state. The EEG datasets are clustered into multiple clusters. A p-adic representation of the clusters is computed. The quantum potential value is extracted from the p-adic representation of the clusters.

Examples of medical states associated with the neuro-psychiatric disorder in a subject which may be diagnosed using a quantum potential value based on EEG datasets obtained from EEG electrodes monitoring a head of a subject (e.g., by comparing the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state) include: depression, schizophrenia, anxiety disorder, Alzheimer's disease, mild cognitive impairment, epileptic seizures, differentiating between no neuro-psychiatric condition and the presence of the neuro-psychiatric condition, differentiating between two different neuro-psychiatric conditions, predicting likelihood of developing the neuro-psychiatric disorder in the future (e.g., when no clinically significant symptoms current exist to diagnose the neuro-psychiatric disorder), predicting likelihood of a clinically significant response to a certain treatment administered for treating the neuro-psychiatric disorder, and/or for evaluating response to a certain treatment applied to the patient for treating the neuro-psychiatric disorder.

The neuro-psychiatric disorders described herein are disorders of the brain, including neurological as well psychiatric disorders. Neuro-psychiatric diseases involve the brain, spinal cord and/or the peripheral nervous system and are primarily even though artificially categorized into neurological and psychiatric disorders. The neurological disorders include, for example, one or more of: cerebrovascular diseases (e.g. stroke), central nervous system trauma, seizure disorders and epilepsy, progressive neurodegenerative diseases (e.g. Alzheimer's dementia, Parkinson's disease, Motor Neuron Disease, Huntington's disease), neuroinflammatory diseases (e.g. Multiple sclerosis, systemic lupus erythematosus), central nervous system tumors, infectious diseases of the nervous system, developmental disorders of the CNS including genetic disorders (Down syndrome, Fragile-x, Autism Spectrum disorder), Acquired metabolic disorders of the CNS, diseases of the CNS caused by malnutrition, toxins or drug abuse. The psychiatric disorders, may be categorized according to the ICD-<NUM>, for example, including organic, including symptomatic, mental disorders, organic amnesic syndrome, personality and behavioral disorder, mental and behavioral disorders due to psychoactive substance use, schizophrenia, schizotypal and delusional disorders, schizoaffective disorders, mood (affective) disorders (e.g. mania, bipolar and depression), neurotic, stress-related and somatoform disorders (e.g. anxiety disorders), somatoform disorders, eating disorders, personality disorders, intellectual disability.

As used herein, the terms neuro-psychiatric disorder and brain disorder are interchangeable.

As used herein, the terms quantum potential, p-adic quantum potential, and/or quantum potential mean and variability score (qpmvs), are sometimes interchangeable. Although the qpmvs is computed from multiple quantum potential values, as described herein, the terms quantum potential, p-adic quantum potential, and/or qpmvs may sometimes refer to the same biomarker used to diagnose a medical state associated with a neuro-psychiatric disorder in a subject. For example, the quantum potential, p-adic quantum potential, and/or qpmvs may each be compared to a threshold to make the diagnosis, as described herein.

At least some implementations of the systems, methods, apparatus, and/or code instructions described herein address the technical problem and/or medical problem of an objective, repeatable, and/or automated process for diagnosing and/or computing a biomarker indicative of likelihood of a brain disorder in a subject, and/or for determining effectiveness of a treatment for the brain disorder. At least some implementations of the systems, methods, apparatus, and/or code instructions described herein improve the technical field and/or the medical field of diagnosing and/or computing a biomarker indicative of likelihood of a brain disorder in a subject, and/or for determining effectiveness of a treatment for the brain disorder.

For most brain disorders no diagnostic tests nor biomarkers are available and diagnosis is made foremost clinically on the basis of history and examination of the mental state by patient interviews in addition to the assessment of level of consciousness, cognition, mood and the neurologic examination (Livingston et al. , <NUM>; Malhi & Mann, <NUM>; Owen et al. , <NUM>, incorporated herein by reference in its entirety). While these tools enabled important decisions in relation to diagnosis, the reliance on a subjective assessment approach prone to patient and expert bias raises questions regarding the accuracy of diagnosis. As a consequence, diagnosis is often delayed until an advanced stage of the disease when possible therapeutic interventions are already lacking effect. It has been estimated that <NUM> to <NUM> out of <NUM> people living with a brain disorder remain untreated or inadequately treated (Collins, Insel, Chockalingam, Daar, & Maddox, <NUM>; Keynejad, Dua, Barbui, & Thornicroft, <NUM>, incorporated herein by reference in its entirety). Therapy, if available, is focused mainly on symptomatic relieve. Numerous drug trials pursuing disease modifying therapy and cure e.g. in Alzheimer's Disease (AD) have failed over the last decades in great parts due to the absence of biomarkers identifying patients in early stages of the disease and the lack of ability to predict who will develop the disease (Mehta, Jackson, Paul, Shi, & Sabbagh, <NUM>; Panza, Lozupone, Logroscino, & Imbimbo, <NUM>; Yiannopoulou, Anastasiou, Zachariou, & Pelidou, <NUM>, incorporated herein by reference in its entirety). Similar to AD, mental disorders (foremost depression and schizophrenia) are needing biomarkers for predicting and identifying patients as well as forecasting treatment responses to pharmacologic interventions or electro-convulsive therapy (ECT) (Kennis et al. , <NUM>; Levy et al. , <NUM>; Strawbridge, Young, & Cleare, <NUM>, incorporated herein by reference in its entirety). As one example, the treatment success rate in schizophrenia can be enhanced if patients at risk are identified, psychotic symptoms are detected timely and treatment is initiated in the prodromal phase (Keefe & Reichenberg, <NUM>; Owen et al. , <NUM>, incorporated herein by reference in its entirety). Identification of people of high risk to develop depression, for example, post traumatic brain injury (TBI), post-trauma, or after major surgery and after giving birth prevention of depression might be an effective strategy (Fang, Scott, Song, Burmeister, & Sen, <NUM>; W. , <NUM>, incorporated herein by reference in its entirety).

A biomarker is a measurable attribute associated with the clinical status of a patient and can be measured objectively and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention (Biomarkers Definitions Working, <NUM>). Much research in regard to biomarkers for brain diseases has been focused on biological samples and imaging techniques. This includes serum or cerebrospinal fluid (CSF) samples and several neuroimaging techniques, including magnetic resonance imaging (MRI), blood oxygenation level-dependent (BOLD) functional MRI (fMRI), and positron emission tomography (PET). Not one of the mentioned techniques has been shown to accurately diagnose such heterogenous disorders like AD (Ferreira et al. , <NUM>; Khoury & Ghossoub, <NUM>; Lebedeva et al. , <NUM>; Ruan et al. , <NUM>; Staffaroni et al. , <NUM>; Wang et al. , <NUM>; Zetterberg & Burnham, <NUM>), MCI, major depression, or schizophrenia (Birur, Kraguljac, Shelton, & Lahti, <NUM>; Kennis et al. , <NUM>; Strawbridge et al. , <NUM>; Zhuo et al. , <NUM>, all of which are incorporated herein by reference in their entirety). The choice of biomarker to be used and possibly support the clinical diagnosis is largely dependent on cost and availability (Ward, <NUM>, incorporated herein by reference in its entirety).

Electroencephalography (EEG) is a non-invasive measure of neuronal electrical activity in the brain recorded non- invasively from electrodes on the scalp. In contrast with other brain imaging methods, EEG has excellent temporal resolution, is non-invasive, of low cost, and currently implemented in healthcare systems around the world due to its current clinical use in helping to diagnose epilepsy (Cervenka & Kaplan, <NUM>; Smith, <NUM>, incorporated herein by reference in its entirety). As diagnostic and prognostic marker EEG is currently mainly used in the context of epileptic seizures as a epileptiform abnormalities increase the likelihood of a seizure recurrence significantly (Krumholz, Shinnar, French, Gronseth, & Wiebe, <NUM>, incorporated herein by reference in its entirety). EEG does play a central role in differentiating primary psychiatric disorders from other brain diseases based on different psychotic syndromes in patients with temporal lobe epilepsy and the high frequency of depression in patients with epilepsy as well as the efficacy of ECT in patients with affective disorders (Endres et al. , <NUM>; Tebartz van Elst et al. , <NUM>; van Elst, <NUM>, incorporated herein by reference in its entirety). Comparable, the reports of epileptiform activity in patients with dementia sparked an interest of using EEG as diagnostic tool for AD (Lam et al. , <NUM>; Noebels, <NUM>; Palop & Mucke, <NUM>, all of which are incorporated herein by reference in their entirety) but subsequent studies demonstrated limited success using epileptiform activity in the EEG to aid with the diagnosis of AD (Cretin et al. , <NUM>; Liedorp, Stam, van der Flier, Pijnenburg, & Scheltens, <NUM>; Vossel et al. , <NUM> all of which are incorporated herein by reference in their entirety). For those reasons, focus has shifted in psychiatric and neurocognitive diseases to EEG signature activity like resting state power spectral and functional connectivity analysis as well as microstate analysis. Successes in using those EEG methods in differentiating patients with a confirmed diagnosis of AD (Amezquita-Sanchez, Mammone, Morabito, Marino, & Adeli, <NUM>; Briels et al. , <NUM>; Michel & Koenig, <NUM>; Smailovic & Jelic, <NUM>; Sun et al. , <NUM>; Tait et al. , <NUM> all of which are incorporated herein by reference in their entirety), Schizophrenia (Baradits et al. , <NUM>; Endres et al. , <NUM>; Maran, Grent-'t-Jong, & Uhlhaas, <NUM>; Oh, Vicnesh, Ciaccio, Yuvaraj, & Acharya, <NUM> all of which are incorporated herein by reference in their entirety), or Depression (Arns & Gordon, <NUM>; Newson & Thiagarajan, <NUM>; Olbrich & Arns, <NUM>; Wade & Iosifescu, <NUM>; C. , <NUM> all of which are incorporated herein by reference in their entirety) have been made but the lack of standardization across the research field raises a strong caution to any clinical application of those findings. A promising approach in the use of EEG to predict treatment responses in major depression has been shown by Wu et al. using a machine learning algorithm for resting-state EEG (W. , <NUM>, incorporated herein by reference in its entirety). As most studies used EEGs in a research setting (standardized situations, open and closed eye separation, reduction of artefacts), the clinical value of the approaches is questionable for routine use in regard to screening, diagnosis and prediction.

At least some implementations of the systems, methods, apparatus, and/or code instructions described provide a biomarker, also referred to herein as a quantum potential, p-adic quantum potential, and/or quantum potential mean and variability score (qpmvs), computed from EEG data obtained from EEG electrodes monitoring a head of a subject. The biomarker provides a computed and/or measurable value indicative of likelihood of a medical state associated with a brain disorder.

Before explaining at least one embodiment in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways in accordance with the appended claims.

The present invention is a system as defined in the appended claims. A computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, and any suitable combination of the foregoing.

Computer readable program instructions for carrying out operations may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

Reference is now made to <FIG>, which is a flowchart of a method of computing a quantum potential value extracted from p-adic representation of clusters of EEG datasets obtained from EEG electrodes monitoring a head of a subject, in accordance with some embodiments. The quantum potential value may be used for diagnosing a medical state associated with a neuro-psychiatric, as described herein and/or in the Experiment in the Examples section below. Reference is also made to <FIG>, which is a block diagram of a system for computing a quantum potential value extracted from p-adic representation of clusters of EEG datasets obtained from EEG electrodes monitoring a head of a subject, in accordance with some embodiments. Reference is also made to <FIG>, which are flowcharts of an exemplary method for quantum potential value from p-adic representation of EEG measurements of a subject, and/or diagnosing the medical state associated with a neuro-psychiatric disorder of the subject according to the quantum potential, in accordance with some embodiments. Reference is also made to <FIG>, which depict experimental results, in accordance with some embodiments. System <NUM> described with reference to <FIG> may implement the features of the method described with reference to <FIG> and/or 3A-3C, by one or more hardware processors <NUM> of a computing device <NUM> executing code instructions stored in a memory (also referred to as a program store) <NUM>. The experimental results of <FIG> may be obtained using the approaches described with reference to <FIG> and/or 3A-3C and/or using components of system <NUM> described with reference to <FIG>.

Computing device <NUM> may be implemented as, for example, a client terminal, a server, a virtual machine, a virtual server, a computing cloud, a mobile device, a desktop computer, a thin client, a Smartphone, a Tablet computer, a laptop computer, a wearable computer, glasses computer, and a watch computer.

Multiple architectures of system <NUM> based on computing device <NUM> may be implemented. In an exemplary implementation of a centralized architecture, computing device <NUM> storing code 206A may be implemented as one or more servers (e.g., network server, web server, a computing cloud, a virtual server) that provides centralized services for computing a quantum potential value and/or diagnosing a subject according to the quantum potential value (e.g., one or more of the acts described with reference to <FIG>) to one or more servers <NUM> and/or client terminals <NUM> over a network <NUM>, for example, providing software as a service (SaaS) to the servers <NUM> and/or client terminal(s) <NUM>, providing software services accessible using a software interface (e.g., application programming interface (API), software development kit (SDK)), providing an application for local download to the servers <NUM> and/or client terminal(s) <NUM>, and/or providing functions using a remote access session to the servers <NUM> and/or client terminal(s) <NUM>, such as through a web browser and/or viewing application. For example, users use client terminals <NUM> to access computing device <NUM> to provide EEG datasets sensed by EEG electrodes monitoring respective heads of subjects. In another example of a localized architecture, code 206A is obtained from computing device <NUM>, and/or locally executed on client terminal <NUM> and/or on server <NUM>. For example, a user may use code 206A executing on client terminal <NUM> to locally compute the quantum potential value and/or locally diagnose subjects based on computed quantum potential values. For example, each EEG lab and/or each psychiatrist installs a local copy of code 206A on their own computer to locally compute the quantum potential value and/or diagnose subjects.

Computing device <NUM> receives EEG dataset captured by EEG electrode(s) <NUM>. EEG electrode(s) <NUM> sense EEG signals of a head of a subject. EEG electrode(s) <NUM> may be wet and/or dry electrodes. EEG electrode(s) <NUM> may be standard EEG electrodes arranged in a standard EEG configuration, for example, as used for detecting epilepsy, performing sleep studies, and the like.

EEG electrode(s) <NUM> may transmit captured EEG datasets (i.e., of EEG signals) to computing device <NUM>, for example, via a direct connected (e.g., local bus and/or cable connection and/or short range wireless connection), and/or via a network <NUM> and a network interface <NUM> of computing device <NUM> (e.g., where EEG electrode(s) are connected via internet of things (IoT) technology and/or are located remotely from the computing device).

Network interface <NUM> may be implemented as, for example, a wire connection (e.g., physical port), a wireless connection (e.g., antenna), a network interface card, a wireless interface to connect to a wireless network, a physical interface for connecting to a cable for network connectivity, and/or virtual interfaces (e.g., software interface, application programming interface (API), software development kit (SDK), virtual network connection, a virtual interface implemented in software, network communication software providing higher layers of network connectivity).

Memory <NUM> stores code instructions executable by hardware processor(s) <NUM>. Exemplary memories <NUM> include a random access memory (RAM), read-only memory (ROM), a storage device, non-volatile memory, magnetic media, semiconductor memory devices, hard drive, removable storage, and optical media (e.g., DVD, CD-ROM). For example, memory <NUM> may code 206A that execute one or more acts of the method described with reference to <FIG> and/or 3A-3C.

Computing device <NUM> may include data storage device <NUM> for storing data, for example, EEG dataset repository 220A for storing EEG datasets captured by EEG electrode(s) <NUM>, for example, where each record of the EEG dataset stores multiple EEG datasets obtained from multiple EEG electrodes sensing a head of a respective subject (e.g., simultaneous recordings). Data storage device <NUM> may store threshold repository 220B which stores different thresholds used to make different diagnoses based on the computed quantum potential value. Data storage device <NUM> may be implemented as, for example, a memory, a local hard-drive, a removable storage unit, an optical disk, a storage device, a virtual memory and/or as a remote server <NUM> and/or computing cloud (e.g., accessed over network <NUM>).

Computing device <NUM> and/or client terminal(s) <NUM> and/or server(s) <NUM> include and/or are in communication with one or more physical user interfaces <NUM> that include a mechanism for inputting data (e.g., enter name of subject, select which disorder is being diagnosed) and/or for viewing data, for example, a display for presenting the computed quantum potential value and/or for presenting the diagnosis. Exemplary user interfaces <NUM> include, for example, one or more of, a touchscreen, a display, a keyboard, a mouse, and voice activated software using speakers and microphone.

Referring now back to <FIG>, an exemplary process for computing a quantum potential value from EEG datasets obtained from EEG electrodes monitoring a head of a subject is now described. The quantum potential value may be used for diagnosing a medical state associated with a neuro-psychiatric, as described herein and/or in the Experiment in the Examples section below.

At <NUM>, EEG datasets are accessed. each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject.

EEG datasets (i.e., EEG measurements) are obtained from multiple EEG electrodes. Raw EEG data from about <NUM>-<NUM>, or <NUM>-<NUM>, or about <NUM> (e.g., as in the Experiment described herein), or other number of active electrodes (elec) may be transformed, for example, to European Data Format (EDF). The EEG data may be filtered to remove <NUM> hertz (Hz) signal. Further data may be filtered with a high pass filter of, for example, <NUM>.

The EEG data may be collected over a sample time interval which may be continuous, for example, about <NUM>-<NUM> seconds, or about <NUM>-<NUM> seconds, or <NUM> seconds (as in the Experiment described herein).

At <NUM>, the EEG datasets may be clustered into clusters, for example, using the following exemplary approach.

Optionally, multiple probability distribution functions are computed. Each respective probability distribution function may be computed for a respective EEG dataset. Clustering may be performed by clustering the probability distribution functions, for example, as described in an exemplary approach below. The p-adic representation is computed based on clusters of the distribution functions, for example, as described in an exemplary approach below.

Optionally, multiple similarity values are computed. Each respective similarity value may be indicative of similarity between a respective pair of the probability distribution functions. Each respective similarity values may be implemented as a Hellinger distance. Clustering may be performed by computing a hierarchical relationship between the pairs of distribution functions according to the similarity values, for example, as described in an exemplary approach below. The p-acid representation is computed according to the hierarchical relationship, for example, as described in an exemplary approach below. Optionally, the hierarchical relationship may be implemented as a dendrogram. The p-acid representation may be computed for each respective route through the dendrogram terminating at an edge corresponding to a certain EEG electrode, for example, as described in an exemplary approach below.

The exemplary approach for clustering EEG datasets, computing the probability distribution functions, computing the probability distribution functions, and/or computing the hierarchical relationship implemented as a dendrogram, is now described:
A moving time window may be selected, for example, about <NUM>, <NUM>, <NUM>, <NUM> seconds, or other time values (<NUM> second was used in the Experiment described herein).

For each time step, the following mathematical relationship is satisfied: <MAT> and each electrode <MAT> a normalized distribution function is constructed. The electrical potential values (p, [mV]) recorded for each electrode (elec) at any given time t may be binned (<NUM>) and/or normalized according to the following mathematical relationship: <MAT>.

A histogram helec,t may be constructed representing an empirical probability distribution function. Alternatively or additionally, the probability distribution function itself is computed. Alternatively or additionally, other data structures may be used to represent the probability distribution function.

Multiple similarity values may be computed for pairs of EEG electrodes, where each similarity value is indicative of similarity between a respective pair of histograms and/or distribution functions. For example, for each t a pairwise Hellinger distance between all <NUM> helec,t is computed. A hierarchical relationship between the pairs of distribution functions (and/or histograms) is computed according to the similarity values, for example, a dendrogram is constructed from the Hellinger distances.

At <NUM>, a p-adic representation of the clusters is computed.

Optionally, the p-adic representation is implemented as <NUM>-adic. Other values may be selected accordingly.

Multiple p-adic representation may be computed. Each respective p-adic representation may correspond to a respective (optionally one) EEG electrode. The quantum potential value is extracted (e.g., as described with reference to <NUM> of <FIG>) by computing a single quantum potential value from the multiple p-adic representations.

Optionally, the p-adic representation is computed from the dendrogram. Each dendrogram may be represented in a matrix (Bt,patient) where each row (relec,t,patient) denotes a p-adic, optionally <NUM>-adic, expansion of the electrode (edge) tree route, for example computed as described with reference to <NPL>, incorporated herein by reference in its entirety.

Optionally, the p-adic representation is converted into a rational number. Each respective p-adic representations may be converted into a corresponding respective rational number. Optionally, each <NUM>-adic expansion (relec,t,patient) may be converted to rational numbers by: <MAT>.

A probability distribution function may be computed from the rational number. Each of multiple probability distribution functions may be computed from a corresponding rational number. For example, a probability distribution function, pdf, ρ(q) from qelec may be computed with kernel function of bandwidth defined by the following mathematical relationship: <MAT>.

At <NUM>, a quantum potential value is extracted from the p-adic representation of the clusters.

The quantum potential value may be computed from the probability distribution function described with reference to <NUM> of <FIG>. Each respective quantum potential value of multiple computed quantum potential values may be computed from the corresponding respective probability distribution function. Each respective quantum potential value may correspond to one of the EEG electrodes.

An aggregation of the quantum potential values may be computed to generate an aggregated quantum potential value, for example, using the exemplary approach described below. The aggregating of the quantum potential values may be computed by computing a mean of the quantum potential values corresponding to the EEG electrodes, for example, using the exemplary approach described below.

The quantum-potential (Qp) may be calculated, for example, according to <NPL>, incorporated herein by reference in its entirety, using the following relationship: <MAT> where h=<NUM>=<NUM> q=qelec q ∈ [<NUM><NUM>]. The integral of the quantum potential may be calculated for each t (and each patient) using the following mathematical relationship: <MAT> For each electrode the quantum potential value may be extracted using the following mathematical relationship: <MAT> The mean of Qelec,t,patient across electrodes in each patient may be defined as Qme.

The a quantum potential value may be computed by extracting a time series that includes multipole quantum potential values each from a corresponding time interval of multiple time intervals during which the EEG datasets are obtained.

For example, the Qme is computed for each time interval, where the multiple Qme values may be represented as a time series. In the Experiment described below, the quantum potential time series data analysis was done with MATLAB® software (Mathworks, Natick, MA).

A quantum potential mean and variability score (qpmvs) may be computed by aggregating the quantum potential values of the time series, for example, as described with reference to <NUM> of <FIG>.

At <NUM>, the medical state of the subject may be diagnose. The medical state may be associated with the neuro-psychiatric disorder. The medical state may be diagnosed according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

The medical state and corresponding non-presence of the medical state may be, for example, a binary outcome indicating whether the subject has a disease or does not have the disease. In another example, the medical state and corresponding non-presence of the medical state may be whether the subject has different states of the disease, such as stable disease or deteriorating disease. In yet another example, the medical state and corresponding non-presence of the medical state may be for two different diseases, where the medical state is a first disease and the non-presence of the medical state is a second disease that is different than the first disease.

In yet another example, the medical state is a prediction of likelihood of developing the disease (e.g., neuro-psychiatric disorder) in the future, and the non-presence of the medical state comprises a prediction of likelihood of not developing the disease (e.g., neuro-psychiatric disorder) in the future, where the subject does not have the disease (e.g., neuro-psychiatric disorder) at the moment. In yet another example, when the subject does have the disease at the moment, the medical state is a prediction of likelihood of developing a certain form of the disease (e.g., deterioration, complication, variant) in the future, and the non-presence of the medical state comprises a prediction of likelihood of not developing the the certain form of the disease in the future.

In yet another example, the medical state is a prediction of likelihood of positively clinically significantly responding to a certain treatment for the disease (e.g., neuro-psychiatric disorder), and the non-presence of the medical state comprises a prediction of likelihood of no clinically significant response to the certain treatment for the disease (e.g., neuro-psychiatric disorder).

The medical state may be, for example: depression, schizophrenia, Alzheimer's disease (AD), and mild cognitive impairment (MCI). The non-presence of the medical state (corresponding to the medical state) may be, for example: no neuro-psychiatric disorder, and another neuro-psychiatric disorder that is different from the medical state.

In another example, the medical state is stable AD and/or stable MCI, and the non-presence of the medical state is deteriorating AD and/or deteriorating MCI.

The medical state may be diagnosed according to the aggregated quantum potential value computed, for example, as described with reference to <NUM> of <FIG>.

The value of the Qme may be used to diagnose the medical state associated with the neuro-psychiatric disorder, for example, a value of the Qme computed for a new patient falling within different ranges may indicate different medical states, for example, as described in the Experiment section below.

Alternatively or additionally, for diagnosing the medical state associated with the neuro-psychiatric disorder using the quantum potential value for a new target subject, a threshold that separates between presence of the medical state and non-presence of the medical state may be computed. The threshold may be set by computing quantum potential values for two patient groups, one of which is known to have the medical state, and the other for which the medical state is not present. The threshold may then be used for new target subjects where the presence or non-presence of the medical state is unknown.

The threshold that separates between presence of the medical state and non-presence of the medical state may be set by computing multiple quantum potential values. Each quantum potential value is for one of multiple subjects associated with an indication of the medical state or an indication of non-presence of the medical state, using respective EEG datasets. The threshold is set to separate between quantum potential values of subjects associated with the indication of the medical state, and quantum potential values of subjects associated with the indication of non-presence of the medical state.

The following exemplary approach may be used for computing the threshold for comparing the two patient groups. For each patient in the two patient groups, the mean log absolute integral of the quantum potential function for all t may be calculated using the following mathematical relationship: <MAT>.

For each patient group, a mean integral of the quantum potential function may be calculated using the following mathematical relationship: <MAT>.

For each patient in the two patient groups, the standard deviation (std) log absolute integral of the quantum potential function for all t may be calculated using the following mathematical relationship: <MAT>.

For any two patient groups to be compared, the mean of the above std may be calculated using the following mathematical relationship: <MAT>.

The quantum potential mean and variability score (qpmvs) may be derived using the following mathematical relationship: <MAT>.

The threshold may be selected by performing a Receiver Operating Characteristic (Roc) analysis on the qpmvspatient. In the Experiment described herein, such analysis was accomplished with MATLAB® software scripts.

Optionally, using a fast Fourier transformation (FFT), a spectrogram may be created for each Qelectrote for each patient frequencies bands of <NUM>-n (n=<NUM>. <NUM>) and a window of for example <NUM> seconds (as used in the Experiment described herein, although other values may be used) with for example <NUM> overlap (as used in the Experiment described herein, although other values may be used). Each patient's electrodes spectrogram (n=<NUM> in the Experiment described herein) may be averaged (<SPelectrode>patient,window) and averaged again across all patients in each group (<<SPelectrode>patient>window). For each frequency band each patient group (<<SPelectrode>patient>window) may be normalized to the maximum value of that particular band in all groups.

Pair-wise t-test and Anova statistical tests were done in the Experiment described herein with MATLAB® and PRISM softwares.

At <NUM>, the subject may be treated for the diagnosed medical state using a treatment effective for the medical state. Exemplary treatments for neuro-psychiatric disorders include: medications (e.g., anti-psychotics, anti-depressants, anti-anxiety), psychiatric and/or psychological therapy (e.g., cognitive behavior therapy), surgery, and electroconvulsive therapy (ECT).

Optionally, the effect of treatment may be evaluated, for example, using the following exemplary approach. A first quantum potential value for the subject is computed prior to administration of a certain treatment for the neuro-psychiatric disorder. The certain treatment is administered to the subject. A second quantum potential value for the subject is computed after the administration of the certain treatment for the neuro-psychiatric disorder. A clinically significant response to the certain treatment is determined when the second quantum potential value is statistically significantly different from the first quantum potential value.

Referring now back to <FIG>, a flowchart depicting an exemplary method for quantum potential computation with output of the integral of the quantum potential function-Qsum and quantum potential value for each electrode-Qelectrode, for example, as described herein, is provided.

At <NUM>, EEG datasets (e.g., recordings) are obtained from EEG electrodes, for example, at least <NUM> electrodes, optionally <NUM> electrodes.

At <NUM>, the EEG datasets are processed. For each electrode, a high pass filter and/or bandpass filter (e. g, highpass filter above <NUM> Hertz (Hz)) and/or notch filters (e.g., <NUM>/<NUM>) are applied to remove <NUM>/<NUM> cycle frequencies (e.g., <NUM>).

At <NUM>, a time window is selected, for example, <NUM> second, or another value.

At <NUM>, the absolute value of time steps windows is evaluated.

At 310A, when there are time step windows which have not yet been processed, the loop in <NUM> is implemented.

At 312A looping is performed over the number of electrodes and looping is performed over the time step windows, by iterating 312B-C. At 312B, each time step window is normalized to the maximum absolute of data values in the current window. At 312C, for each time step window, a histrogram and/or pdf is constructed from normalized data values.

Alternatively at 310A, at 310B when there are no time step windows remaining to be processed, the loop of <NUM> is terminated.

At <NUM>, output of a three dimensional matrix with size (n,m,k) is provided. Where n denotes the number of electrodes, m denotes the number of time step windows, and k denotes the number of bins in the histrogram and/or pdf.

At <NUM>, a loop of features 316A-G is iterated.

At 316A, an agglomerative hierarchical cluster from pair-wise Hellinger distances between all electrode histrogram and/or pdf output is performed to obtain an agglomerative hierarchical cluster tree.

At 316B, a p-acid representation matrix representing each tree is constructed.

At 316C, output of a <NUM> dimensional matrix denoted [A] having size (n,k) is obtained. Where n denotes the number of electrodes, and k denotes the maximal length of each electrode p-adic expansion.

At 316D, for each row denoted a in matrix A, the following is computed: <MAT>.

At 316E, a kernel distribution function is constructed from the b values computed in 316D, with a kernel smoothing function denoted K, and bandwidth denoted h. An exemplar value of the bandwidth h=(max(electrodes values) - min(electrodes values))/(k)<NUM>.

At 316F, a de Broglie-Bohm quantum potential denoted Q is computed over the interval [min(b) max(b)]. Each electrode is assigned its own value Qelectrode. The intergral of the de Broglie-Bohm quantum potential denoted Qsum is computed over the interval.

At <NUM>, 316A-F are iterated over m, where m denotes the number of time step windows.

Referring now back to <FIG>, is a flowchart depicting an exemplary method for computation of a threshold (denoted "th") that separates two groups of subjects according to quantum potential mean and/or variability score (denoted "S"), is provided. <FIG> also relates to computation of mean of S for each group <NUM> and <NUM>, standard deviation (std) and mean of log10(abs(Qsum)) for each group <NUM> and <NUM>.

Features ending in "A" are performed for group <NUM>, and features ending in "B" are performed for group <NUM>.

At 340A, a first group with a certain number of members is defined. At 340B a second group with another number of members is defined. The first group may be include subjects with the medical state and the second group may include subjects with non-presence of the medical state, for example, as described herein.

At 342A, Qsum (i.e., integral of the quantum potential function) and Qelectrode (i.e., quantum potential value for each electrode) are computed for a member of group <NUM>, for example, as described with reference to <FIG>. At 344A, 342A is iterated for each member of group <NUM>, i.e., Qsum and Qelectrode are computed for each member of group <NUM>. At 342B and 344B, Qsum and Qelectrode are computed for each member of group <NUM>.

At 346A, the mean of the quantum potential mean and/or variability score, denoted Qmean_group1 and the standard deviation of the quantum potential mean and/or variability score denoted Qstd_group1 are computed for each subject of group <NUM>.

Qmean_group1 is computed according to the following equation: <MAT>.

Qsd_group1 is computed according to the following equation: Qstd_group1=std(10long(abs(Qsum)).

At 346B, the mean of the quantum potential mean and/or variability score are computed for each subject of group2, i.e., Qmean_group2 and Qstd_group2.

At <NUM>, the standard deviation of Qstd_group1 and Qstd_group2 is computed.

At 350A, the mean of the mean value of quantum potential values computed for each subject of group <NUM>, denoted mean(Qmean_group1), is computed.

At 350A, the mean of the mean value of quantum potential values computed for each subject of group <NUM>, denoted mean(Qmean_group2), is computed.

At 352A, a vector is computed for a subject of group <NUM>, according to the following equation: <MAT>.

At 354A, feature 352A is computed for each subject of group <NUM>.

At 356A, as a result of 352A and 354A, a vector denoted S with n number of entries of Vgroup1*Qmean_group1 is computed.

At 352B, a vector is computed for a subject of group <NUM>, according to the following equation: <MAT>.

At 354B, feature 352B is computed for each subject of group <NUM>.

At 356B, as a result of 352B and 354B, a vector denoted S with m number of entries of Sgroup1=Vgroup2*Qmean_group2 is computed.

At <NUM>, a Receiver Operating Characteristic (Roc) analysis is performed between the two groups to obtain the best threshold (th) for the above score denoted S, that separates between the two groups.

One or more of the following outputs may be provided: Sgroup1, Sgroups2, th of scores, std(Qstd_group1&Qstd_group2), mean(Qmean_group1), and mean(Qmean_group2).

Referring now back to <FIG>, a flowchart of an exemplary method for diagnosing a new subject, by classifying the subject as belonging to either group <NUM> (e.g., having the medical state, such as associated with a neuro-psychiatric disorder) or to group <NUM> (e.g., having the non-medical state), is provided. The classification of the new subject is based on computation of quantum potential mean and variability score of the new subject, for example, as in the flowchart described with reference to <FIG> and/or 3A. The subject is classified as belonging to group <NUM> or group <NUM>, for example, according to the output values of the flowchart described with reference to <FIG>. The "Output" of the method of <FIG> denotes the determination to which group the new subject belongs to.

One or multiple approaches may be used to determine whether the subject is classified into group <NUM> (in which case the subject is diagnosed with the medical state of group <NUM>) or into group <NUM> ((in which case the subject is diagnosed with the medical state of group <NUM>, which may be a non-medical state).

402A-B represent conditions that are evaluated to determine whether the condition is met (i.e., yes 408A) or not met (i.e., no 408B), to determine whether the subject is a member of group1 404A or group2 404B.

At 402A, R is computed according the equation: R=(median(Qmean_group1) - median(Qmean_group2))/<NUM>. The following condition is evaluated to determine whether the condition is met (i.e., yes 408A) or not met (i.e., no 408B): <MAT>.

At 402B, R is computed according the equation: R=(median(Qmean_group1) - median(Qmean_group2))/<NUM>. The following condition is evaluated to determine whether the condition is met (i.e., yes 408A) or not met (i.e., no 408B): <MAT>.

406A-E represent other conditions that are evaluated to determine whether the condition is met (i.e., yes 408A) or not met (i.e., no 408B), to determine whether the subject is a member of group1 404A or group2 404B.

At 406C, the condition is mean(Sgroup1) > th.

Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental and/or computational support in the following examples.

Reference is now made to the following examples, which together with the above descriptions illustrate at least some implementations of systems, methods, apparatus, and/or code instructions described herein.

Inventors conducted an experiment, which was designed and carried out following the guidelines of the Declaration of Helsinki and approved by the local Ethics Committee Rabin Medical Center, Petach Tikva Israel and Geha Mental Health Center, Petach Tikva, Israel. Patients were selected from the EEG database of the Rabin Medical Center between <NUM> and <NUM>.

Using the online medical health records of the Rabin Medical Center all patients undergoing at least one routine EEG examination at the Rabin Medical Center were included and categorized into four separate groups according to the following criteria. Inventors included <NUM> patients (average age: <NUM> ±<NUM> years; <NUM>-<NUM> years; <NUM> females). Due to the heterogeneity of the included patients including controls, all included subjects are declared herein further on as patients.

Routine Electroencephalogram (EEG) recordings were collected from all patients retrospectively. EEG was performed in a routine clinical setting and all patients included underwent EEG in the morning hours between 8am and 1pm using a Nihon Koden surface EEG (<NUM>-electrodes standard international <NUM>-<NUM> electrode placement) with a sampling frequency of <NUM>. EEG was performed by an experienced technician.

The quantum potential value for each electrode was computed (e.g., Qme) from the quantum potential function, as described herein. The computed quantum potential enabled comparing the defined groups of patients.

Referring now back to <FIG>, graphs depicting results of the experiment indicative of P-adic quantum potential (QP) differentiating between neuropsychiatric patient groups, are provided.

<FIG> and <FIG> depict that the mean of the cumulative distribution function (CDF) of the mean Qme across patients was significantly different for all patient groups defined (p<1e-<NUM>; Anova). Curve <NUM> denotes control (i.e., no psychiatric condition), also referred to as con. Curve <NUM> denotes schizophrenia, also referred to as schiz. Curve <NUM> denotes depression, also referred to as dep. Curve <NUM> denotes mild cognitive impairment, also referred to as mci. Curve <NUM> denotes Alzheimer's, also referred to as alz. In control patients (n=<NUM>) the mean of the CDF of the mean Qme across patients was <NUM> ±<NUM> and was highly significantly different from patients with depression (n=<NUM>; <NUM> ±<NUM>; p=<NUM>. 0087e-<NUM>), schizophrenia (n=<NUM>; <NUM> ±<NUM>; p=<NUM>. 1279e-<NUM>), AD (n=<NUM>; <NUM> ±<NUM>; p=<NUM>. 2120e-<NUM>), and MCI (n=<NUM>; <NUM> ±<NUM>; p=<NUM>. 7661e-<NUM>). Interestingly, the variability of the CDF denoted as standard deviation (SD) of the mean Qme across patients was also significantly different in every patient group compared to control (p<le-<NUM>), as depicted in <FIG> and <FIG>.

The Qme across groups of patients (control, AD, MCI, depression, and schizophrenia) is was compared. Regarding the mean of the CDF of the mean Qme all patient groups were separated significantly from each other, as depicted in the heatmap of FIG. The variability of the CDF of the mean Qme was significantly different and thus separating all patient groups except direct comparison of AD and MCI, as depicted in the heatmap of <FIG>.

Referring now back to <FIG>, graphs depicting additional results of the experiment indicative of QP cross correlation between patient's electrodes, are provided.

The correlation heatmaps of FIGs. <NUM>, <NUM>, and <NUM> are divided into squares on an x-y axis, where the first square on the x-axis (on the left bottom corner) starts at <NUM>, and the last square on the x-axis (on the right) is <NUM>. The first square on the y-axis (on the left bottom corner) starts at <NUM>, decreasing to the last square on the y-axis (on the top) labelled <NUM>. Legend <NUM> indicates a scale, staring at <NUM> (darkest) to <NUM> (lightest), shown in increments of <NUM>.

Following the theorized non-locality of the QP according to de-broglie-bohm theory (e.g., as described with reference to <NPL>, incorporated herein by reference in its entirety) the pair-wise cross correlation was examined at the same time points between each of the patient's EEG electrodes QP (Qelec,t,patient). Pair-wise cross correlation analysis between each of the patient's electrodes time series characterizes the non-local instantaneous effect between a pair of electrodes. These non-local influences are distinctively and significantly different between patient groups and represented by correlation <NUM> heatmap of <FIG> and summarized for comparison, as depicted in graph <NUM>. To identify non-instantaneous interactions between each of the patient's EEG electrodes QP (Qelec,t,patient) the maximal absolute correlation coefficient between each of the patient's electrodes over the whole recording time was examined. This represents the local non-instantaneous effect between pairs of electrodes as depicted in heatmap <NUM> and summarized for comparison as depicted in graph <NUM>. To characterize the temporal relationship between two electrodes QP (Qelec,t,patíent) the time lag (tlag) between two EEG electrodes maximal correlation coefficient was identified as depicted in heatmap <NUM> and summarized for comparison in graph <NUM>.

Referring now back to <FIG>, graphs depicting yet additional results of the experiment in terms of QP power spectrum analysis of Qelectrote values, are provided.

To characterize the QP of each patient group further, the power spectrum of Qelectrote values of each group were compared using FFT as described herein. The normalized power density spectrum analysis was significantly different for all groups (p<<NUM>; Anova). Normalized power density spectrum analysis of control patients (<NUM>± <NUM>) were significantly different from those of patients with depression (<NUM>± <NUM>; p= <<NUM>) and schizophrenia (<NUM>± <NUM>; p=<<NUM>). Normalized power density spectrum analysis of patients with AD (<NUM>± <NUM>) was significantly different from patients with MCI (<NUM>± <NUM>; p=<NUM>) but AD and MCI did not differ from control patient groups (p=<NUM> and p=<NUM> respectively, as depicted in <FIG>.

Referring now back to <FIG>, graphs depicting yet additional results of the experiment indicative of diagnosing and/or predicting disease time course of neuro-psychiatric patients using QP, are provided.

The quantum potential mean and variability score (qpmvs) for each patient was computed as described above. Receiver Operating Characteristic (Roc) analysis using the qpmvs of each patient shows the accuracy level of the score. Evaluation of diagnostic tests is a matter of concern in modern medicine not only for confirming the presence of disease but also to rule out the disease in healthy subjects. As the plot of sensitivity versus <NUM>-Specifity is called receiver operating characteristic (ROC) curve and the area under the curve (AUC), as an effective measure of accuracy has been considered with a meaningful interpretation. Using the qpmvs solely based on routine EEG recordings the predictive accuracy to identify (categorize) patients with an unknown diagnosis of neuropsychiatric spectrum (healthy, depressed, schizophrenia, cognitive decline like AD and MCI) by ROC analysis and AUC was evaluated. Firstly, the control patient group was evaluated against all neuropsychiatric disease groups. The ROC analysis showed a high test accuracy when comparing control patients versus patients with schizophrenia (AUC= <NUM>± <NUM>; p= <<NUM>), control patients versus patients with depression (AUC= <NUM>± <NUM>; p= <<NUM>), control patients versus patients with AD (AUC= <NUM>± <NUM>; p= <<NUM>), and control patients versus patients with MCI (AUC= <NUM>± <NUM>; p= <<NUM>) as depicted in <FIG>.

Referring now back to <FIG>, additional graphs depicting yet additional results of the experiment indicative of diagnosing and/or predicting disease time course of neuro-psychiatric patients using QP, are provided.

Using the quantum potential mean and variability score (qpmvs) for each patient, Inventors discovered that patients with neuropsychiatric disorders could not only be separated accurately from the control patients but also from each other, making the qpmvs a useful diagnostic test to predict a specific diagnosis. Comparing the neuropsychiatric patient groups to each other patients with schizophrenia versus depression (AUC= <NUM>± <NUM>; p= <<NUM>), schizophrenia versus AD (AUC= <NUM>± <NUM>; p= <<NUM>), depression versus AD (AUC= <NUM>± <NUM>; p= <<NUM>), depression versus AD (AUC= <NUM>± <NUM>; p= <<NUM>), depression versus MCI (AUC= <NUM>± <NUM>; p= <<NUM>), and schizophrenia versus MCI (AUC= <NUM>± <NUM>; p= <<NUM>) as depicted in <FIG>.

When focusing on the subgroup of patients with cognitive decline (AD and MCI), Inventors discovered an accuracy of separation between the patient groups which was suboptimal (AUC= <NUM>± <NUM>; p < <NUM>). The MCI patient groups were analyzed according to their disease course and separated the MCI patients in one group which showed a stable disease course (stbMCI) versus those patients with a deteriorating disease course (detMCI). The two groups did not differ in age or cognitive testing scores when first evaluated and examined by EEG recording. They were all clinically classified as MCI without any signs possibly predicting their clinical course (stbMCI versus detMCI: <NUM>± <NUM> years versus <NUM>± <NUM> years; p=<NUM>; <NUM>±<NUM> MMSE versus <NUM>±<NUM> MMSE; p=<NUM>). Using the qpmvs the stbMCI and detMCI were separated highly accurately (AUC= <NUM>± <NUM>; p= <<NUM>). The MCI patients with a stable disease course showed clear separation from patients with AD using the qpmvs (AUC= <NUM>± <NUM>; p= <<NUM>) while those deteriorating did not separate from the AD patients (AUC= <NUM>± <NUM>; p= <NUM>) as shown in <FIG>.

The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

It is expected that during the life of a patent maturing from this application many relevant EEG datasets will be developed and the scope of the term EEG dataset is intended to include all such new technologies a priori.

Claim 1:
A system for diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising:
at least one processor executing a code for:
receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject;
clustering the plurality of EEG datasets into a plurality of clusters;
computing a p-adic representation of the plurality of clusters;
extracting a quantum potential value from p-adic representation of the plurality of clusters; and
diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.