Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Hsiu, Hsin; Lin, Shun‐Ku; Weng, Wan-Ling; Hung, Chaw-Mew; Chang, Chi Cheng; Lee, Chia-Chien; Chen, Chao-Tsung

doi:10.3390/s22030806

Cited by 11 publications

(12 citation statements)

References 26 publications

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“…For example, analyses of HR and BP variability have been used to investigate changes related to diseases and aging 23 – 25 . Similarly, variability of frequency-domain BPW indices ( CV n and P n _ SD ) can be used to monitor the regulatory efforts induced by changes in the blood flow or vascular stiffness condition 21 , 22 .…”

Section: Discussionmentioning

confidence: 99%

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Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

OuYoung

Chiu

et al. 2022

Sci Rep

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Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia.

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Section: Discussionmentioning

confidence: 99%

“…Details of the present experimental setup and the signal processing methods have been reported previously 21 , 22 and are also available in the Supplemental Materials (Supplementary Information 1 ). Blood pressure waveform (BPW) and ECG signals were measured noninvasively (typical waveforms are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

OuYoung

Chiu

et al. 2022

Sci Rep

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“…Details of the present experimental setup and the signal processing methods are available elsewhere [ 20 , 21 , 22 , 23 ] and in the Supplemental Materials. Blood pressure waveform (BPW) signals of the subjects were measured noninvasively (typical waveforms are shown in Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Analyses Revealed Distinct Arterial Pulse Variability According to Side Effects of Pfizer-BioNTech COVID-19 Vaccine (BNT162b2)

Chen

Chang

Chiu

et al. 2022

JCM

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Various adverse events and complications have been attributed to COVID-19 (coronavirus disease 2019) vaccinations, which can affect the cardiovascular system, with conditions such as myocarditis, thrombosis, and ischemia. The aim of this study was to combine noninvasive pulse measurements and frequency domain analysis to determine if the Pfizer-BioNTech COVID19 vaccine (BNT162b2) vaccination and its accompanying cardiovascular side effects will induce changes in arterial pulse transmission and waveform. Radial blood pressure waveform and photoplethysmography signals were measured noninvasively for 1 min in 112 subjects who visited Shuang-Ho Hospital for a BNT162b2 vaccination. Based on side effects, each subject was assigned to Group N (no side effects), Group CV (cardiac or vascular side effects), Group C (cardiac side effects only), or Group V (vascular side effects only). Two classification methods were used: (1) machine-learning (ML) analysis using 40 harmonic pulse indices (amplitude proportions, phase angles, and their variability indices) as features, and (2) a pulse-variability score analysis developed in the present study. Significant effects on the pulse harmonic indices were noted in Group V following vaccination. ML and pulse-variability score analyses provided acceptable AUCs (0.67 and 0.80, respectively) and hence can aid discriminations among subjects with cardiovascular side effects. When excluding ambiguous data points, the AUC of the score analysis further improved to 0.94 (with an adopted proportion of around 64.1%) for vascular side effects. The present findings may help to facilitate a time-saving and easy-to-use method for detecting changes in the vascular properties associated with the cardiovascular side effects following BNT162b2 vaccination.

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“…The conventional diagnostic assessment of dementia involves medical history, clinical examinations (e.g., neurological, mental state, and cognitive examinations) and an interview with a relative other than the informant [ 5 ]. Current early-stage dementia diagnosis is based on pathological characteristics or cognitive diagnostic tests.…”

Section: Introductionmentioning

confidence: 99%

“…H. Hsiu et al studied ML algorithms for early identification of cognitive impairment. Their proposed model obtained an accuracy of 70.32% by threefold cross-validation scheme [ 5 ]. Several classification models were constructed using different ML models and feature selection methodologies to automate MCI detection based on gait biomarkers.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning

Javeed

Dallora

Berglund

et al. 2022

Life

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Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.

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Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Cited by 11 publications

References 26 publications

Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

Machine Learning Analyses Revealed Distinct Arterial Pulse Variability According to Side Effects of Pfizer-BioNTech COVID-19 Vaccine (BNT162b2)

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning

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