Identification of Mental Disorders by Hidden Markov Modeling of Photoplethysmograms

Chen, Ying; Oyama‐Higa, Mayumi; Pham, Tuan D.

doi:10.1007/978-3-642-54121-6_3

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…The signal waves of the BVP sensor are shown in Figure 7 c, where S is the systolic valley, M is the systolic peak, P is the dicrotic notch and Q is the diastolic peak [ 41 ]. The measured values of the M-M interval, pulse width and area, M-Q interval, pulse interval, systolic amplitude, augmentation index, stiffness index and diastolic amplitude were used along with their means and standard deviations as the features from BVP signals [ 41 , 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Alam

Abedin

Ameen

et al. 2016

Sensors

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Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study.

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Section: Methodsmentioning

confidence: 99%

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Alam

Abedin

Ameen

et al. 2016

Sensors

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show abstract

“…Then, the results are fed to the network as features for pattern recognition. Chen et al [25] proposed a hidden Markov model for PPG classification. They first used linear predictive coding and sample entropy methods to extract different features from the PPG waveforms.…”

Section: Neural Network In Ppg Applicationsmentioning

confidence: 99%

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

Kazemi

Laitala

Azimi

et al. 2022

Sensors

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Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.

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“…Then, the results are fed to the network as features for pattern recognition. Chen et al [21] proposed a hidden Markov model for PPG classification. They first used linear predictive coding and sample entropy methods to extract different features from the PPG waveforms.…”

Section: Neural Network In Ppg Applicationsmentioning

confidence: 99%

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

Kazemi¹,

Laitala²,

Azimi³

et al. 2021

Preprint

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<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

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Identification of Mental Disorders by Hidden Markov Modeling of Photoplethysmograms

Cited by 6 publications

References 27 publications

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

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