Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-Threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-Time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-Tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal. 2013 IEEE.Corresponding authors: Muhammad E. H. Chowdhury ([email protected]), Mamun Bin Ibne Reaz ([email protected]), and Md. Shafayet Hossain ([email protected]) This work was supported in part by the Qatar National Research under Grant NPRP12S-0227-190164, and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes. 2020 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors.Scopu

Water Quality and Plankton Composition of Amblypharyngodon mola Monoculture Fish Pond in Bangladesh

A study was conducted to assess the water quality and plankton composition in Amblypharyngodon mola fish pond for a period of 4 months in Bangladesh. Nine earthen pond each with three treatments, viz. T1, T2 and T3 were stocked with A. mola at the density of 145,000; 73,000 and 36,500 individual ha-1, respectively. Water quality parameters such as water temperature, transparency, total alkalinity, pH, dissolved oxygen, Nitrate- nitrogen, ammonia-nitrogen, phosphate-phosphorus and chlorophyll-a of the ponds water were measured. Water quality parameters (except transparency and chlorophyll-a) did not show any significant differences (P> 0.05) among the treatments. The lowest PO4-P and chlorophyll-a concentration were observed in treatment T1 where 145,000 individual ha-1 of A. mola was cultured. Plankton samples were also collected and identified throughout the study period. A total of 38 genera of phytoplankton and 13 genera of zooplankton were identified of which Chlorophyceae (20 genera) in phytoplankton population and Crustacea (9 genera) in zooplankton population were dominant. The mean value of total plankton population (× 103 cells L-1) were 158.42 53.33, 191.17 62.24 and 240.17 93.37 in T1, T2 and T3 treatments, respectively and contributing to the fish production according to their availability and abundance within the treatment. The study reveals that the rural based farmers can develop an actual mechanism of plankton production in aquatic environment which could be essential necessary for the maintenance of water quality and sustainable development of small scale indigenous fish culture in Bangladesh and other developing countries

Community Engagement in The Telehealth Service for Aged People with Diabetes: COVID-19 response in Bangladesh

Purpose: The purpose of this study is to present a better understanding of the specialized telehealth service in Bangladesh from the service provider and service recipients by aged people Method: Both quantitative and qualitative methods were used to collect data from Diabetes Mellitus (DM) patients. Data were collected by online telephone interviewing with an interview schedule. A total of 100 aged people with diabetes were selected purposively for a quantitative interview and 10 In-depth Interviews (IDIs) & Key Informant Interviews (KIIs) were conducted. Result: The majority of patients aged was between 61 to 68 years with a mean age of 63.6 ± 7.01years. The difference of age of DM patients by sex was found statistically significant (x2 = 39.49, df = 31; Cramer’s V = .032; P=<.003). The main source of information about digital health was: relatives (55%), neighbors (31%), television (12%), newspaper (10%), social media (9%), and healthcare providers (6%). Strong relationship was found between age of respondents and sources of information (x2= 77.08; Cramer’s V= .032, df = 13; Sig; P= < .009). About 59% of DM patients were benefited from telehealth services during COVID-19, however; they encountered some difficulties like effective access to digital technology, cost, and diagnosis facilities. About 83% of respondents suggest formalizing community engagement programs to extend the digital health services during a health emergency. The common barriers to the engagement of community people in digital health care are lack of social awareness, lack of peer group support, and gender disparities. Poor counseling, language barrier, bad internet signal, and lack of family members' support were the key barriers during teleconsultation services. Conclusion: Telehealth has the potential to address critical health issues of aged people and effective community engagement may be the best option to reach older people with diabetes in Bangladesh during any health emergency

Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals

Gait analysis is helpful for rehabilitation, clinical diagnoses, and sporting activities. Among the gathered signals, ground reaction forces (GRF) may be used for assisting doctors in recognizing and categorizing gait patterns using Machine-Learning methods. In this study, GaitRec and Gutenberg databases were used, where GaitRec contains 2645 gait disorder (GD) patients and 211 Healthy Controls (HCs), and the Gutenberg database has 350 HCs. The combined database has HCs and four GD classes: hip, knee, ankle, and calcaneus. GD is an abnormality in the hip, knee, or ankle joints, whereas Calcaneus gait is calcaneus fractures or ankle fusions. We pre-processed the GRF signals, applied different feature extraction techniques, removed the highly correlated features, and ranked the features using three feature selection algorithms. K-nearest neighbour model (KNN) showed the top performance in terms of accuracy in all experiments. Four different experimental schemes were pursued: (i) 6 binary classifications; (ii) 1 three-class classification; (iii) 2 four-class classifications; (iv) one five-class classification. We also compared the performance of vertical GRF with three-dimensional GRF. We found that using three-dimensional GRF increased the overall performance. Furthermore, it is found that time-domain and Wavelet features are among the most useful in identifying gait patterns. The findings show promising performance in automated gait disorder classification. 2022 Elsevier LtdThis work was made possible by Qatar National Research Fund (QNRF) NPRP12S-0227-190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021-001 and Universiti Kebangsaan Malaysia under Grant GUP-2021-019 and DPK-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

Community People Preparedness and Response on Prevention and Control of COVID-19 Best Practice in Bangladesh

Purpose: The major objectives of the study were to assess the knowledge, attitude & practice (KAP) towards community preparedness and response on prevention of COVID-19 among the community people. Method: A sample survey was conducted to collect data from people admitted to a district-level tertiary hospital for the treatment of various health complications during the COVID-19 pandemic. A total of 300 randomly selected patients and their attendants were interviewed in the hospital setting. Results: The mean knowledge score was 18.73 out of 24 and the main sources of information were TV (86.5%), radio (13%), newspaper (13%), social media (13.5%), friends/relatives (14%), formal healthcare providers (6%) and religious leaders (3%). Knowledge was significantly poor among aged people, women, less educated, and less earning. The majority of the participants (79%) suggested wearing facemasks as effective tools to prevent COVID-19 from spreading, whereas 56% mentioned maintaining physical or social distance as crucial to prevent the infection. We found strong relationship between monthly total family expenses and wearing of facemasks by gender to prevent the COVID-19 (x2= 18.405; Cramer’s V= .17, df = 8; sig; P= < .018). Similarly maintaining physical/social distance to prevent COVID-19 is also related to respondents’ economic strata (x2= 43.741; Cramer’s V= .14, df = 20; Sig; P= < .002). Conclusions: Awareness program on COVID-19 is very important to prevent the spread of the deadly virus.  Effective communication intervention with increasing treatment facilities is essential for the prevention and control of COVID-19. Government and development agencies should prioritize the COVID-19 response program with regular health care services.