Analysis of Bio-signal Data of Stroke Patients and Normal Elderly People for Real-Time Monitoring

Seo

2021

Sensors

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Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

Section: Introductionmentioning

confidence: 99%

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Seo

2021

Sensors

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“…The highest severity score is marked as 42, and the lowest is labeled as 0. Out of the total of 48 stroke patients studied in this study, seven patients experienced a minor stroke (NIHSS: 1-4), ten patients were identified with a moderate stroke (NIHSS: 5-15), 13 patients diagnosed with a moderate-to-severe stroke (NIHSS: [16][17][18][19][20], and eighteen patients had a severe stroke (NIHSS: 21-42). The control group comprises healthy adults without any history of ischemic or hemorrhagic stroke or underlying known neurologic disorders.…”

Section: Demographics Of Participantsmentioning

confidence: 99%

“…During the rehabilitation of stroke patients, EEG changes can help to track the post-stroke recovery in daily life and clinical setup. Plentiful EEG studies were reported to investigate EEG in medical and wellness settings to explore the correlation between the EEG features and the neurological outcomes after ischemic stroke [ 5 , 7 , 13 , 14 , 15 , 16 , 17 , 18 ]. The rise of slow-wave delta activity and the decline of fast-wave activity were found as predictive markers of reduced functional outcomes in the lesion area, and the nonexistence of these phenomena was reported as excellent mental outcomes [ 3 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Task-Induced Neurological Outcome after Stroke

2021

Brain Sciences

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Electroencephalography (EEG) can access ischemic stroke-derived cortical impairment and is believed to be a prospective predictive method for acute stroke prognostics, neurological outcome, and post-stroke rehabilitation management. This study aims to quantify EEG features to understand task-induced neurological declines due to stroke and evaluate the biomarkers to distinguish the ischemic stroke group and the healthy adult group. We investigated forty-eight stroke patients (average age 72.2 years, 62% male) admitted to the rehabilitation center and seventy-five healthy adults (average age 77 years, 31% male) with no history of known neurological diseases. EEG was recorded through frontal, central, temporal, and occipital cortical electrodes (Fz, C1, C2, T7, T8, Oz) using wireless EEG devices and a newly developed data acquisition platform within three months after the appearance of symptoms of ischemic stroke (clinically confirmed). Continuous EEG data were recorded during the consecutive resting, motor (walking and working activities), and cognitive reading tasks. The statistical results showed that alpha, theta, and delta activities are biomarkers classifying the stroke patients and the healthy adults in the motor and cognitive states. DAR and DTR of the stroke group differed significantly from those of the healthy control group during the resting, motor, and cognitive tasks. Using the machine-learning approach, the C5.0 model showed 78% accuracy for the resting state, 89% accuracy in the functional motor walking condition, 84% accuracy in the working condition, and 85% accuracy in the cognitive reading state for classification the stroke group and the control group. This study is expected to be helpful for post-stroke treatment and post-stroke recovery.

“…Supervised machine learning techniques are an efficient tool for classification and discovering patterns in a dataset. In previous studies, machine learning was successfully utilized to classify the physiological [21,31] and behavioral [20,32] data of the stroke dataset and the control dataset. Machine learning and deep learning techniques are also utilized to classify the fatigue indies [33] and sleep apnea [34] Besides, the testing data size was 156 sets of HRV features and 1697 sets of fiducial features.…”

Section: G Classificationmentioning

confidence: 99%

Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management

2021

IEEE Access

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Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF+HF), and HF/(LF+HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.INDEX TERMS Cyber-physical systems, Electrocardiography, Biomedical monitoring, Big data applications, Biomedical informatics.