المؤلفون: Jan Øystein Berle, Lena Antonsen Stabell, Pål Halvorsen, Ketil J. Oedegaard, Ole Bernt Fasmer, Vajira Thambawita, Enrique Garcia-Ceja, Michael Riegler, Petter Jakobsen, Steven Alexander Hicks

المصدر: CBMS

مصطلحات موضوعية: medicine.medical_specialty, Schizophrenia (object-oriented programming), Cognition, Actigraphy, Mental health, 030227 psychiatry, Clinical trial, 03 medical and health sciences, 0302 clinical medicine, Replication (statistics), medicine, Motor activity, Psychiatry, 030217 neurology & neurosurgery, Reliability (statistics)

الوصف: Using sensor data from devices such as smart-watches or mobile phones is very popular in both computer science and medical research. Such movement data can predict certain health states or performance outcomes. However, in order to increase reliability and replication of the research, it is important to share data and results openly. In medicine, this is often difficult due to legal restrictions or to the fact that data collected from clinical trials is seen as very valuable and something that should be kept "in-house". In this paper, we therefore present PSYKOSE, a publicly shared dataset consisting of motor activity data collected from body sensors. The dataset contains data collected from patients with schizophrenia. Schizophrenia is a severe mental disorder characterized by psychotic symptoms like hallucinations and delusions, as well as symptoms of cognitive dysfunction and diminished motivation. In total, we have data from 22 patients with schizophrenia and 32 healthy control persons. For each person in the dataset, we provide sensor data collected over several days in a row. In addition to the sensor data, we also provide some demographic data and medical assessments during the observation period. The patients were assessed by medical experts from Haukeland University hospital. In addition to the data, we provide a baseline analysis and possible use-cases of the dataset.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9f6c0eae171389c76315be98352c494Test
https://doi.org/10.31219/osf.io/e2tzfTest

المؤلفون: Enrique Garcia-Ceja, Petter Jakobsen, Tine Nordgreen, Lena Antonsen Stabell, Ole Bernt Fasmer, Michael Riegler, Jim Torresen, Ketil J. Oedegaard

المصدر: e0231995
PLOS ONE
15:e0231995
PLoS ONE
PLoS ONE, Vol 15, Iss 8, p e0231995 (2020)

مصطلحات موضوعية: Male, Bipolar Disorder, Emotions, Social Sciences, Audiology, Overfitting, Convolutional neural network, Standard deviation, Machine Learning, Cognition, Learning and Memory, 0302 clinical medicine, Medicine and Health Sciences, Psychology, Statistical Data, Multidisciplinary, Artificial neural network, Depression, Applied Mathematics, Simulation and Modeling, Statistics, Physical Sciences, Memory Recall, Medicine, Female, Algorithms, Research Article, Adult, Computer and Information Sciences, medicine.medical_specialty, Neural Networks, Science, Motor Activity, Research and Analysis Methods, Sensitivity and Specificity, Machine Learning Algorithms, 03 medical and health sciences, Artificial Intelligence, Memory, Rating scale, Mental Health and Psychiatry, medicine, Humans, Generalizability theory, Bipolar disorder, Depressive Disorder, Major, Mood Disorders, business.industry, Biology and Life Sciences, medicine.disease, 030227 psychiatry, Mood, Cognitive Science, Neural Networks, Computer, business, Mathematics, 030217 neurology & neurosurgery, Neuroscience

الوصف: 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. publishedVersion

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::482921b8418a1f9489b5ff499f3615e2Test
http://hdl.handle.net/10852/80958Test

المؤلفون: Erlend Eindride Fasmer, Erik R Hauge, Petter Jakobsen, Zahra Sepasdar, Vigdis Elin Giæver Syrstad, Tone E G Henriksen, Jan Øystein Berle, Kristin Mjeldheim, Wenche Førland, Ole Bernt Fasmer, Ketil J. Oedegaard

المصدر: PLoS ONE, Vol 15, Iss 11, p e0241991 (2020)
PLoS ONE
e0241991
PLOS ONE
15:e0241991

مصطلحات موضوعية: Male, Entropy, Information Theory, Social Sciences, Audiology, Medical Conditions, 0302 clinical medicine, Medicine and Health Sciences, Psychology, Morning, Multidisciplinary, Cognitive Neurology, Depression, Physics, Applied Mathematics, Simulation and Modeling, Middle Aged, Anxiety Disorders, Circadian Rhythm, Neurology, Physical Sciences, Thermodynamics, Medicine, Anxiety, Female, medicine.symptom, Algorithms, Research Article, Adult, Computer and Information Sciences, medicine.medical_specialty, Evening, Adolescent, Cognitive Neuroscience, Science, Neuropsychiatric Disorders, Motor Activity, Neuroses, Research and Analysis Methods, Impulsivity, Young Adult, 03 medical and health sciences, Developmental Neuroscience, Neuropsychology, Mental Health and Psychiatry, medicine, Humans, Aged, Neuropsychological Testing, Behavior, Mood Disorders, business.industry, Diurnal temperature variation, Biology and Life Sciences, Graph theory, 030227 psychiatry, Mood, Attention Deficit Disorder with Hyperactivity, Neurodevelopmental Disorders, Graph Theory, Cognitive Science, Adhd, Diurnal Variations, business, Mathematics, 030217 neurology & neurosurgery, Neuroscience

الوصف: 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. publishedVersion

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c9a240b2d008a66f0d22d35260c9c28Test
https://doi.org/10.1371/journal.pone.0241991Test

المؤلفون: Ole Bernt Fasmer, Steven C. Dilsaver, Ketil J. Oedegaard, Jan Øystein Berle, Erik R Hauge

المصدر: Psychiatry Investigation

مصطلحات موضوعية: medicine.medical_specialty, Depression, Physiology, Actigraphy, Motor behavior, medicine.disease, Motor activity, 030227 psychiatry, 03 medical and health sciences, Psychiatry and Mental health, 0302 clinical medicine, Schizophrenia, medicine, Distribution (pharmacology), In patient, Original Article, Psychiatry, Psychology, 030217 neurology & neurosurgery, Biological Psychiatry, Depression (differential diagnoses)

الوصف: Objective Alterations of activity are prominent features of the major functional psychiatric disorders. Motor activity patterns are characterized by bursts of activity separated by periods with inactivity. The purpose of the present study has been to analyze such active and inactive periods in patients with depression and schizophrenia. Methods Actigraph registrations for 12 days from 24 patients with schizophrenia, 23 with depression and 29 healthy controls. Results Patients with schizophrenia and depression have distinctly different profiles with regard to the characterization and distribution of active and inactive periods. The mean duration of active periods is lowest in the depressed patients, and the duration of inactive periods is highest in the patients with schizophrenia. For active periods the cumulative probability distribution, using lengths from 1 to 35 min, follows a straight line on a log-log plot, suggestive of a power law function, and a similar relationship is found for inactive periods, using lengths from 1 to 20 min. For both active and inactive periods the scaling exponent is higher in the depressed compared to the schizophrenic patients. Conclusion The present findings add to previously published results, with other mathematical methods, suggesting there are important differences in control systems regulating motor behavior in these two major groups of psychiatric disorders.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32c5487c89c2e35252e1aca315bfc774Test
http://europepmc.org/articles/PMC4701674Test

المؤلفون: Jim Torresen, Ketil J. Oedegaard, Petter Jakobsen, Ole Bernt Fasmer, Tine Nordgreen, Enrique Garcia-Ceja, Michael Riegler

المصدر: MMSys

مصطلحات موضوعية: medicine.medical_specialty, Computer science, Wearable computer, Demographic data, medicine.disease, Field (computer science), 030227 psychiatry, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Rating scale, medicine, Bipolar disorder, Motor activity, Association (psychology), 030217 neurology & neurosurgery, Depression (differential diagnoses)

الوصف: Wearable sensors measuring different parts of people's activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients' depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::b2b6420d32c42f94b984b22e49f31d38Test
https://doi.org/10.1145/3204949.3208125Test

المؤلفون: Petter Jakobsen, Michael Riegler, Jim Torresen, Tine Nordgreen, Ole Bernt Fasmer, Enrique Garcia-Ceja, Ketil J. Oedegaard

المصدر: CBMS

مصطلحات موضوعية: medicine.medical_specialty, business.industry, media_common.quotation_subject, Wearable computer, Mental health, 030227 psychiatry, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Mood, Medicine, Personality, Motor activity, business, F1 score, 030217 neurology & neurosurgery, Depression (differential diagnoses), media_common

الوصف: Wearable sensors measuring different parts of people's activity are a common technology nowadays. Data created using these devices holds a lot of potential besides measuring the quantity of daily steps or calories burned, since continuous recordings of heart rate and activity levels usually are collected. Furthermore, there is an increasing awareness in the field of psychiatry on how these activity data relates to various mental health issues such as changes in mood, personality, inability to cope with daily problems or stress and withdrawal from friends and activities. In this paper we present the analysis of a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. We apply machine learning to classify patients into depressed and nondepressed. For evaluation of the algorithms, leave one patient out validation is performed. The best results achieved are an F1 score of 0.73 and a MCC of 0.44. The overall findings show that sensor data contains information that can be used to determine the depression status of a person.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::e5fe373b93e6b6903cc078dfa5bf2533Test
https://doi.org/10.1109/cbms.2018.00062Test

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

المصدر: PLoS ONE
PLoS ONE, Vol 13, Iss 4, p e0194791 (2018)

مصطلحات موضوعية: Male, Bipolar Disorder, Entropy, lcsh:Medicine, Nervous System, 0302 clinical medicine, Medicine and Health Sciences, Graph algorithms, lcsh:Science, Multidisciplinary, Depression, Physics, Applied Mathematics, Simulation and Modeling, Middle Aged, Physical Sciences, Thermodynamics, Graph (abstract data type), Female, Anatomy, Psychology, Algorithms, Research Article, Adult, Computer and Information Sciences, Motor behavior, Motor Activity, Research and Analysis Methods, Young Adult, 03 medical and health sciences, Diagnostic Medicine, Mental Health and Psychiatry, Humans, In patient, Motor activity, Aged, Psychiatric Status Rating Scales, Mood Disorders, business.industry, lcsh:R, Biology and Life Sciences, Graph theory, Pattern recognition, Models, Theoretical, Motor System, 030227 psychiatry, Graph Theory, Psychiatric status rating scales, Schizophrenia, lcsh:Q, Artificial intelligence, business, Mathematics, 030217 neurology & neurosurgery, Neuroscience

الوصف: Depression and schizophrenia are defined only by their clinical features, and diagnostic separation between them can be difficult. Disturbances in motor activity pattern are central features of both types of disorders. We introduce a new method to analyze time series, called the similarity graph algorithm. Time series of motor activity, obtained from actigraph registrations over 12 days in depressed and schizophrenic patients, were mapped into a graph and we then applied techniques from graph theory to characterize these time series, primarily looking for changes in complexity. The most marked finding was that depressed patients were found to be significantly different from both controls and schizophrenic patients, with evidence of less regularity of the time series, when analyzing the recordings with one hour intervals. These findings support the contention that there are important differences in control systems regulating motor behavior in patients with depression and schizophrenia. The similarity graph algorithm we have described can easily be applied to the study of other types of time series.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18965fa9f0d21be814181fe4b675e882Test
https://doi.org/10.1371/journal.pone.0194791Test

المؤلفون: Erik R Hauge, Jan Øystein Berle, Fred Holsten, Ole Bernt Fasmer, Ketil J. Oedegaard

المصدر: BMC Research Notes
BMC Research Notes, Vol 3, Iss 1, p 149 (2010)

مصطلحات موضوعية: medicine.medical_specialty, Short Report, lcsh:Medicine, Audiology, General Biochemistry, Genetics and Molecular Biology, medicine, In patient, Motor activity, lcsh:Science (General), Psychiatry, lcsh:QH301-705.5, Relative amplitude, Clozapine, Depression (differential diagnoses), Medicine(all), Biochemistry, Genetics and Molecular Biology(all), business.industry, lcsh:R, Actigraphy, General Medicine, medicine.disease, lcsh:Biology (General), Schizophrenia, business, lcsh:Q1-390, medicine.drug

الوصف: Background Disturbances in motor activity pattern are seen in both schizophrenia and depression. However, this activity has rarely been studied objectively. The purpose of the present study has been to study the complexity of motor activity patterns in these patients by using actigraphy. Findings Motor activity was recorded using wrist-worn actigraphs for periods of 2 weeks in patients with schizophrenia and major depression and compare them to healthy controls. Average motor activity was recorded and three non-parametric variables, interdaily stability (IS), intradaily variability (IV), and relative amplitude (RA) were calculated on the basis of these data. The motor activity was significantly lower both in patients with schizophrenia (153 ± 61, mean ± SD, p < 0.001) and depression (187 ± 84, p < 0.001), compared to controls (286 ± 80). The schizophrenic patients had higher IS and lower IV than the controls reflecting a more structured behavioural pattern. This pattern was particularly obvious in schizophrenic patients treated with clozapine and was not found in depressed patients. Conclusions Motor activity was significantly reduced in both schizophrenic and depressed patients. However, schizophrenic patients differed from both depressed patients and controls, demonstrating motor activity patterns marked by less complexity and more structured behaviour. These findings may indicate that disturbances in motor activity reflect different pathophysiological mechanisms in schizophrenia compared to major depression.

وصف الملف: application/pdf; text/xml

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88cacca9f9092d78b7c611b64f6653d7Test
https://doi.org/10.1186/1756-0500-3-149Test

المؤلفون: Kristin Mjeldheim, Ole Bernt Fasmer, Anita L. Hansen, Steven C. Dilsaver, Jan Øystein Berle, Ketil J. Oedegaard, Wenche Førland

المصدر: Psychiatry Investigation

مصطلحات موضوعية: medicine.medical_specialty, Core (anatomy), Adult patients, Actigraph, Audiology, medicine.disease, Motor activity, Fourier analysis, Developmental psychology, Psychiatry and Mental health, Rhythm, Autocorrelation, medicine, ADHD, Attention deficit hyperactivity disorder, Daily living, Original Article, Psychology, Biological Psychiatry

الوصف: Objective Hyperactivity is a core symptom of attention-deficit hyperactivity disorder (ADHD), but limited information is available on analysis of activity patterns in this disorder. The aim of the study was to analyze motor activity during daily living in adult patients with ADHD. Methods Patients (n=76) from the private psychiatric practice of two of the authors were recruited, and were compared to patients with other psychiatric disorders and to normal controls. Actigraphs were used to record motor activity for six days, with one minute intervals, and data were analysed using linear and non-linear mathematical methods. Results For short recording periods (300 minutes) the activity levels of ADHD patients do not differ from normal controls, but the autocorrelation (lag 1) is lower and Fourier analysis shows higher power in the high frequency range, corresponding to the period from 2-8 min. During recordings for six days there are no significant differences between ADHD patients and the control groups. The combined and inattentive subgroups differ only in the six days recordings. The Fourier analyses show that the combined type has lower power in the high frequency range, corresponding to the period from 4-8 hours, and in the analysis of rhythms the intra-daily variability is lower, compared to the inattentive type. Conclusion Adult ADHD patients do not show evidence of hyperactivity, but have levels of activity similar to normal controls. However, on several measures ADHD patients display altered activity patterns, indicating that the regulation of motor activity in this disorder is different from controls.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e25a5e321f5d40205ca754ea407253f4Test
https://doi.org/10.4306/pi.2015.12.4.474Test

المؤلفون: Fred Holsten, Erik R Hauge, Ketil J. Oedegaard, Jan Øystein Berle, Ole Bernt Fasmer

المصدر: PLoS ONE
PLoS ONE, Vol 6, Iss 1, p e16291 (2011)

مصطلحات موضوعية: Male, Central Nervous System, Time Factors, Anatomy and Physiology, Entropy, lcsh:Medicine, Audiology, Medical disciplines: 700::Clinical medical disciplines: 750::Psychiatry, child psychiatry: 757 [VDP], Standard deviation, lcsh:Science, Psychiatry, Movement Disorders, Multidisciplinary, Fourier Analysis, Depression, Middle Aged, Mental Health, Neurology, Fourier analysis, Observational Studies, symbols, Medicine, Female, Period length, Research Article, Nervous System Physiology, Adult, medicine.medical_specialty, Clinical Research Design, Neurophysiology, Motor Activity, Biology, Neurological System, Young Adult, symbols.namesake, medicine, Humans, In patient, Motor activity, Aged, Motor Systems, Mood Disorders, lcsh:R, Case-control study, Sample entropy, Nonlinear Dynamics, Case-Control Studies, Schizophrenia, lcsh:Q, Physiological Processes, Chronobiology, Neuroscience

الوصف: The purpose of this study has been to describe motor activity data obtained by using wrist-worn actigraphs in patients with schizophrenia and major depression by the use of linear and non-linear methods of analysis. Different time frames were investigated, i.e., activity counts measured every minute for up to five hours and activity counts made hourly for up to two weeks. The results show that motor activity was lower in the schizophrenic patients and in patients with major depression, compared to controls. Using one minute intervals the depressed patients had a higher standard deviation (SD) compared to both the schizophrenic patients and the controls. The ratio between the root mean square successive differences (RMSSD) and SD was higher in the schizophrenic patients compared to controls. The Fourier analysis of the activity counts measured every minute showed that the relation between variance in the low and the high frequency range was lower in the schizophrenic patients compared to the controls. The sample entropy was higher in the schizophrenic patients compared to controls in the time series from the activity counts made every minute. The main conclusions of the study are that schizophrenic and depressive patients have distinctly different profiles of motor activity and that the results differ according to period length analysed. publishedVersion

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40a2bd4ff146162b2374195e387436aaTest
https://doi.org/10.1371/journal.pone.0016291Test