Abnormal lateralization of motor activity during sleep in schizophrenia

Lauerma, Hannu; Niskanen, Leo; Lehtinen, I.; Holmström, Reijo

doi:10.1016/0920-9964(94)90010-8

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…The influence of laterality on results remains unclear. Lauerma et al [17] reported abnormal lateralization of motor activity during sleep in schizophrenia. This phe nomenon has not been studied in depression.…”

Section: Discussionmentioning

confidence: 99%

Motor Activity and Daily Variation of Symptom Intensity in Depressed Patients

Lemke

Broderick²,

Zeitelberger³

et al. 1997

Neuropsychobiology

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Spontaneous motor activity measured by actigraphy and subjectively experienced intensity of symptoms were investigated in 16 unipolar depressed inpa-tients with melancholic features. Patients felt significantly less active, awake, and more depressed in the morning compared to the evening. However, morning activity levels appeared significantly higher compared to evening levels. Diurnal variations of symptoms were reflected by actigraphically measured motor activity, which was negatively correlated with subjectively experienced symptom intensity. Increased motor activity may represent an observable, psychobiological, behavioral equivalent of self-experienced depressive symptoms in major depression with melancholic features.

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Section: Discussionmentioning

confidence: 99%

Motor Activity and Daily Variation of Symptom Intensity in Depressed Patients

Lemke

Broderick²,

Zeitelberger³

et al. 1997

Neuropsychobiology

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“…Literature Source Mean activity & Standard Deviation [22], [21], [52], [53], [21], [39], [54], [55], [38], [48], [33] Interdaily Stability(IS) & Intradaily Variability (IV) [38], [21], [56], [57] Least active 5 hours (L5) & Most active 10 hours (M10) [21], [56] Root mean square successive differences (RMSSD) [34], [55] Autocorrelation coefficient [55] Table 1: Overview of quantitative features of actigraphy time series found in the literature.…”

Section: Features For Baseline Classificationmentioning

confidence: 99%

Prediction of schizophrenia from activity data using hidden Markov model parameters

Boeker

Hammer

Riegler

et al. 2022

Neural Comput & Applic

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In this paper, we address the problem of predicting schizophrenia based on a persons measured motor activity over time. A key challenge to achieve this is how to extract features from the activity data that can efficiently separate schizophrenia patients from healthy subjects. To achieve this, we suggest to fit time dependent hidden Markov models with and without integrated covariates and letting the estimated model parameters represent our features. To further evaluate the efficiency of these features, we suggest to use them as features in a classification method (logistic regression) to separate schizophrenia patients from healthy subjects. The results show that the estimated hidden Markov model parameters are well-performing in predicting schizophrenia, and outperform features derived from other methods in the literature in terms of goodness-of-fit and classification performance.

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“…Literature Source Mean activity & Standard Deviation [4], [14], [18], [29], [30], [32], [37], [45]- [47] Inter-daily Stability (IS) & Intra-daily Variability (IV) [18], [29], [48], [49] Root mean square successive differences (RMSSD) [5], [46] Autocorrelation coefficient [46] 2) Classification Procedure: To classify between schizophrenia and control, we use the trained parameters from the HMM as features in a logistic regression model.…”

Section: Features For Baseline Classificationmentioning

confidence: 99%

Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters

Boeker

Riegler

Hammer

et al. 2021

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)

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The diagnosis of Schizophrenia is mainly based on qualitative characteristics. With the usage of portable devices which measure activity of humans, the diagnosis of Schizophrenia can be enriched through quantitative features. The goal of this work is to classify between schizophrenic and non-schizophrenic subjects based on their measured activity over a certain amount of time. To do so, the periods in which a subject was resting or active were identified by the application of a Hidden Markov model (HMM). The trained model parameters of the HMM, such as the mean or variance of activity during the state of rest or activity, are used as classification features for a logistic regression model. Our results indicate that the features from the HMM are significant in classifying between schizophrenic and non-schizophrenic subjects. Moreover, the features outperform the features derived through other methods in literature in terms of goodness-of-fit and classification performance.

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Abnormal lateralization of motor activity during sleep in schizophrenia

Cited by 11 publications

References 15 publications

Motor Activity and Daily Variation of Symptom Intensity in Depressed Patients

Motor Activity and Daily Variation of Symptom Intensity in Depressed Patients

Prediction of schizophrenia from activity data using hidden Markov model parameters

Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters

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