Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters

Boeker, Matthias; Riegler, Michael; Hammer, Hugo Lewi; Halvorsen, Pål; Fasmer, Ole Bernt; Jakobsen, Petter

doi:10.1109/cbms52027.2021.00048

Cited by 6 publications

(6 citation statements)

References 46 publications

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“…Their proposed method performed a cut above the rest of the deployed machine learning techniques. Boeker et al 44 proposed hidden Markov model (HMM) parameters to classify healthy controls and schizophrenia patients. The work aimed to classify non-schizophrenic and schizophrenic participants based on the HMM, and the results showed that the features of the HMM were outperforming other models in terms of classifying non-schizophrenic and schizophrenic participants.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Nguyen

Chan

et al. 2022

Health Informatics J

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In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients’ families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

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Section: Literature Reviewmentioning

confidence: 99%

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Nguyen

Chan

et al. 2022

Health Informatics J

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“…To gain a better understanding of the sleep/wake pattern, HMMs can be used to extract these patterns. HMMs have already been applied for this purpose [43,23,44].…”

Section: Related Workmentioning

confidence: 99%

“…Boeker et al [23] applied HMMs to describe the sleep/wake pattern as two states. The HMM parameters are extracted and used as features for logistic regression.…”

Section: Related Workmentioning

confidence: 99%

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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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“…In the case of continuation in receiving data points and the interest in finding hidden states by modeling observable sequential data, HMM is a capable model. HMM has been used in various medical applications [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. Moreover, it has been applied in human activity recognition [ 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with an HMM-Based Generative Model

Manouchehri

Bouguila

2023

Sensors

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Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

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Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters

Cited by 6 publications

References 46 publications

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Prediction of schizophrenia from activity data using hidden Markov model parameters

Human Activity Recognition with an HMM-Based Generative Model

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