Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients

Garcia-Ceja, Enrique; Riegler, Michael; Jakobsen, Petter; Tørresen, Jim; Nordgreen, Tine; Oedegaard, Ketil J.; Fasmer, Ole Bernt

doi:10.1109/cbms.2018.00062

Cited by 35 publications

(57 citation statements)

References 30 publications

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“…With this data, we aim to detect which group participants are in, and also their severity of depression. The primary motivation for these experiments comes from earlier work done by Garcia-Ceja, E. et al ( [15]), where they used ML to detect depression on the same participants. However, our usage of ML is different, and whether our performance is any better is a topic of discussion.…”

Section: Motivationmentioning

confidence: 99%

“…The authors of the paper mentioned first surveying different machine learning research in MHMS, Garcia-Ceja, E. et al, also released a paper on motor activity based classification of depression in unipolar/bipolar patients [15]. They applied machine learning for classifying depressed/nondepressed participants using Random Forest and a DNN.…”

Section: Mental Health Monitoring Systemsmentioning

confidence: 99%

“…CNN is a type of machine learning that is more sophisticated than the ones used in the mentioned research papers. We know that motor activity measurements can be related to mental health issues (from Garcia-Ceja, E. et al [15]). However, the best methods for extracting this type of knowledge is not known.…”

Section: Mental Health Monitoring Systemsmentioning

confidence: 99%

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One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Frogner

Noori

Halvorsen

et al. 2019

Proceedings of the 4th International Workshop on Multimedia for Personal Health &Amp; Health Care

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Teaching machines to learn patterns in data is very common these days, and it has a broad spectrum of applications everywhere. Sensors like smart-watches are getting more functionality each year, and more and more people buy them. Passing data from the watches, for example, activity or heart rate to machine learning algorithms, can generate significant results within many fields. Mental health is an example of a field where computer-generated predictions can be helpful to gain knowledge about patients. For example, machine learning can help predict that someone has a specific type of mental disorder. In this thesis, we present applied machine learning to detect depression. The dataset (collected for another study about behavioral patterns in schizophrenia vs. major depression) contains motor activity measurements for each minute in the measured period for each participant. Three machine learning models are trained to fit time-sliced segments of these measurements. The first model classifies participants into condition group (depressed) and control group (non-depressed). We trained another model to classify the depression level of participants (normal, mild or moderate). Finally, we trained a model that predicts MADRS scores. We evaluate the performance of classification models using leave one participant out validation as a technique, in which we achieved an average F1-score of 0.70 for detecting control/condition group and 0.30 for detecting the depression levels. The MADRS score prediction resulted in a mean squared error of approximately 4.0. These performance scores are promising, but not good enough to be used in the real world. However, not much more work is needed for the first model if we apply it to a dataset with more participants.

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Section: Motivationmentioning

confidence: 99%

Section: Mental Health Monitoring Systemsmentioning

confidence: 99%

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One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Frogner

Noori

Halvorsen

et al. 2019

Proceedings of the 4th International Workshop on Multimedia for Personal Health &Amp; Health Care

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“…The basic framework of our ML approach has earlier been presented in a technological conference paper (35), but the method presented here represents a substantial extension of the previous work. Given that the main objective was to classify a user as depressed or not depressed, we proposed the following approach to accomplish this: Each user collected data for d i consecutive days where d i represents the number of days collected by participant i .…”

Section: Methodsmentioning

confidence: 99%

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients

Jakobsen

Garcia-Ceja

Riegler

et al. 2020

Preprint

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23 24 25 2 26 ABSTRACT 27 Current practice of assessing mood episodes in affective disorders largely depends on 28 subjective observations combined with semi-structured clinical rating scales. Motor activity is 29 an objective observation of the inner physiological state expressed in behavior patterns. 30 Alterations of motor activity are essential features of bipolar and unipolar depression. The aim 31 was to investigate if objective measures of motor activity can aid existing diagnostic practice, 32 by applying machine-learning techniques to analyze activity patterns in depressed patients and 33 healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network34 algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 35 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were 36 mean activity, standard deviation of mean activity and proportion of zero activity. Various 37 techniques to handle data imbalance were applied, and to ensure generalizability and avoid 38 overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as 39 measures of accuracy for binary tests. A Deep Neural Network combined with random 40 oversampling class balancing technique performed a cut above the rest with a true positive 41 rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and 42 the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data 43 overlapping among the classes, so an appropriate future approach will be to compare mood 44 states intra-individualistic. In summary, machine-learning techniques present promising 45 abilities in discriminating between depressed patients and healthy controls in motor activity 46 time series. 47 48 49 50 3 51 Introduction 52 The current practice of assessing mood episodes in affective disorders are subjective 53 observations combined with semi-structured clinical rating scales. Objective methods for 54 assessing affective symptoms are desired (1). Motor activity is an objective observation of the 55 inner physiological state expressed in behavior patterns, and alterations in activation are 56 essential symptoms of bipolar and unipolar depression (2, 3). The depressive state is typically 57 associated with reduced daytime motor-activity, increased variability in activity levels and 58 less complexity in activity patterns compared to healthy controls (2). However, in some 59 bipolar and unipolar depressed patients contradictory motor activity patterns have been 60 observed, characterized by increased mean activity levels, reduced variability and an 61 augmented complexity in activity patterns more similar to that observed in manic patients (4). 62 Such depressions are commonly associated with irritability, restlessness, and aroused inner 63 tension, in contrast to the general loss of initiative and interest characterizing psychomotor 64 retarded depressions (5). 65 It has been suggested by Sabelli et al. (6) that moo...

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“…It helps to save time spent on travel and monitoring in person and also gives precise diagnosis of the illness. [1].This paper helps to classify the patients based on particular symptoms.Analyse the classification algorithms in order to specify the patients having unipolar or bipolar disorder. [2].Understanding of the human and social behaviour of the patients based on smartphones.Gathering of information through sensors and it monitored by the smartphones.…”

Section: Iintroductionmentioning

confidence: 99%

Vital Symptoms Monitoring and Classification of Bipolar Disorder

Devi,

Kaviya

2019

IJEAT

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Bipolar disorder is a mental illness that puts patients into extreme states of mind known as mania and depression. These mental states are very harmful to the lives of the patients as their day to day actions are disrupted. This project aims at identifying the symptoms of the patients who have extreme moods to determine if they are bipolar using sensors and smart phones. Patients with mental illness tend to exhibit symptoms like reduced physical activity, changes in mood, drastic changes in sleep pattern, inability to cope with stress and withdrawn from socializing. These changes can be monitored using sensors and the data collected is compared with the data collected from healthy individuals. A classification algorithm is applied to the data collected to classify the symptoms and detect if the person has bipolar disorder or is just showing subtle signs of mood swings.

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Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients

Cited by 35 publications

References 30 publications

One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients

Vital Symptoms Monitoring and Classification of Bipolar Disorder

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