Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression

Fasmer, Erlend Eindride; Fasmer, Ole Bernt; Berle, Jan Øystein; Oedegaard, Ketil J.; Hauge, Erik R.

doi:10.1371/journal.pone.0194791

Cited by 22 publications

(20 citation statements)

References 30 publications

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“…In this study we apply a heuristic algorithm that is nonlinear and not chaos-based that transforms a time series S = (x 1 , x 2 , …, x n ) into a similarity graph G = (V,E), an undirected graph, where each node u ∈ V = {1, 2, …, n } corresponds to the element x u ∈ S and where the node u is assigned a weight equal to the value of x u [ 44 ]. The distance between two nodes u and v , is | u — v |.…”

Section: Methodsmentioning

confidence: 99%

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Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory

Fasmer

Fasmer²,

Mjeldheim³

et al. 2020

PLoS ONE

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Attention-deficit /hyperactivity disorder (ADHD) is a common neurodevelopmental syndrome characterized by age-inappropriate levels of motor activity, impulsivity and attention. The aim of the present study was to study diurnal variation of motor activity in adult ADHD patients, compared to healthy controls and clinical controls with mood and anxiety disorders. Wrist-worn actigraphs were used to record motor activity in a sample of 81 patients and 30 healthy controls. Time series from registrations in the morning and evening were analyzed using measures of variability, complexity and a newly developed method, the similarity algorithm, based on transforming time series into graphs. In healthy controls the evening registrations showed higher variability and lower complexity compared to morning registrations, however this was evident only in the female controls. In the two patient groups the same measures were not significantly different, with one exception, the graph measure bridges. This was the measure that most clearly separated morning and evening registrations and was significantly different both in healthy controls and in patients with a diagnosis of ADHD. These findings suggest that actigraph registrations, combined with mathematical methods based on graph theory, may be used to elucidate the mechanisms responsible for the diurnal regulation of motor activity.

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Section: Methodsmentioning

confidence: 99%

“…. ., n} corresponds to the element x u 2 S and where the node u is assigned a weight equal to the value of x u [44]. The distance between two nodes u and v, is |u -v|.…”

Section: Similarity Graphmentioning

confidence: 99%

Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory

Fasmer

Fasmer²,

Mjeldheim³

et al. 2020

PLoS ONE

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“…This is a reanalysis of motor activity recordings originating from an observational cohort study presented in previous papers [12,13,30]. The study group consisted of 23 bipolar and unipolar outpatients and inpatients at Haukeland University Hospital, Bergen, Norway.…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…Complex dynamical systems rarely categorizes by simple linear models. Therefore, mathematical tools obtained from the field of non-linear complex and chaotic systems have been the traditional method for analyzing and evaluating motor activity recordings [ 12 – 14 ]. Machine learning (ML) techniques have displayed promising results in analyzing data of complex dynamical systems [ 15 , 16 ], and MLs ability to reveal non-obvious patterns has fairly accurately classified mood state in long-term heart rate variability analysis of bipolar patients [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls

et al. 2020

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Current practice of assessing mood episodes in affective disorders largely depends on subjective observations combined with semi-structured clinical rating scales. Motor activity is an objective observation of the inner physiological state expressed in behavior patterns. Alterations of motor activity are essential features of bipolar and unipolar depression. The aim was to investigate if objective measures of motor activity can aid existing diagnostic practice, by applying machine-learning techniques to analyze activity patterns in depressed patients and healthy controls. Random Forrest, Deep Neural Network and Convolutional Neural Network algorithms were used to analyze 14 days of actigraph recorded motor activity from 23 depressed patients and 32 healthy controls. Statistical features analyzed in the dataset were mean activity, standard deviation of mean activity and proportion of zero activity. Various techniques to handle data imbalance were applied, and to ensure generalizability and avoid overfitting a Leave-One-User-Out validation strategy was utilized. All outcomes reports as measures of accuracy for binary tests. A Deep Neural Network combined with SMOTE class balancing technique performed a cut above the rest with a true positive rate of 0.82 (sensitivity) and a true negative rate of 0.84 (specificity). Accuracy was 0.84 and the Matthews Correlation Coefficient 0.65. Misclassifications appear related to data overlapping among the classes, so an appropriate future approach will be to compare mood states intra-individualistically. In summary, machine-learning techniques present promising abilities in discriminating between depressed patients and healthy controls in motor activity time series.

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“…This is a reanalysis of motor activity recordings originating from a study presented in previous papers (12, 13, 31). The study group consisted of 23 bipolar and unipolar outpatients and inpatients at Haukeland University Hospital, Bergen, Norway.…”

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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Graph theory applied to the analysis of motor activity in patients with schizophrenia and depression

Cited by 22 publications

References 30 publications

Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory

Diurnal variation of motor activity in adult ADHD patients analyzed with methods from graph theory

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls

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

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