Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

Congenital heart diseases (CHDs), caused by structural abnormalities in the heart and blood vessels, pose a significant public health concern and contribute significantly to the socioeconomic burden, particularly in pediatric populations. Phonocardiograms (PCGs), as a non-invasive and cost-effective diagnostic modality, capture vital acoustic signals that reflect the mechanical activity of the heart and can reveal pathological patterns associated with various CHD types. This study investigates the minimum signal duration required for accurate automatic classification of heart sounds and evaluates signal quality using the root mean square of successive differences (RMSSD) and the zero-crossing rate (ZCR). Mel-frequency cepstral coefficients (MFCCs) are extracted as features and fed into a transformer-based residual one-dimensional convolutional neural network (1D-CNN) for classification. Experimental results show that a threshold of 0.4 for RMSSD and ZCR yields optimal classification performance, with a minimum signal length of 5 seconds required for reliable results. Shorter segments (3 seconds) lack sufficient diagnostic information, while longer segments (15 seconds) may introduce additional noise. The proposed model achieves a maximum classification accuracy of 93.69% with 5-second signals.

The phonocardiogram (PCG) or heart sound auscultation is a low-cost and non-invasive method to diagnose Congenital Heart Disease (CHD). However, recognizing CHD in the pediatric population based on heart sounds is difficult because it requires high medical training and skills. Also, the dependency of PCG signal quality on sensor location and developing heart in children are challenging. This study proposed a deep learning model that classifies unprocessed or raw PCG signals to diagnose CHD using a one-dimensional Convolution Neural Network (1D-CNN) with an attention transformer. The model was built on the raw PCG data of 484 patients. The results showed that the attention transformer model had a good balance of accuracy of 0.923, a sensitivity of 0.973, and a specificity of 0.833. The Receiver Operating Characteristic (ROC) plot generated an Area Under Curve (AUC) value of 0.964, and the F1-score was 0.939. The suggested model could provide quick and appropriate real-time remote diagnosis application in classifying PCG of CHD from non-CHD subjects.Clinical Relevance— The suggested methodology can be utilized to analyze PCG signals more quickly and affordably for rural doctors as a first screening tool before sending the cases to experts.

Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due to limited skilled experts, whose ability to interpret pathological patterns varies significantly, causing inter- and intra-clinician variability. Therefore, we present a new method for a more accessible diagnostic modality, the digital stethoscope, to detect CHDs. Our method is based on deep feature fusion, integrating deep and handcrafted features for the automated early detection of CHDs. For this work, Phonocardiography (PCG) recordings were obtained from 751 pediatric subjects (Age:1 month- 16 years) in Bangladesh, ranging from infants to adults at four auscultation locations: mitral valve (MV), aortic valve (AV), pulmonary valve (PV), and tricuspid valve (TV). These recordings were labeled based on confirmed diagnoses by cardiologists as either cases of CHD or non-CHD. The results demonstrated that our proposed model achieved an accuracy of 92%, a sensitivity of 91%, and a specificity of 91%, based on a patient-wise split of 70% training, 20% validation, and 10% testing. Furthermore, the Area Under the Receiver Operating Characteristic curve (AUROC) of 96%, and an F1-score of 92%. This model promises efficient real-time remote detection of CHDs as a cost-effective screening tool for low-resource settings.

A serious supply shortage of electricity has created in Bangladesh because electricity generation capacity didn't increase proportionately to the demand; there exist 6000 MW maximum electricity shortage during hot summer days and also in the harvesting and irrigation season. Besides to generate power environment is being deteriorated more and more globally. In this circumstances there is no alternative of renewable energy and Bangladesh have a great opportunity to generate electricity from geothermal energy to overcome this power crisis. North-east region of this country is suitable for planting geothermal power plant. The geothermal gradient at south-east region varies from 19.8 to 29.5°C/km along the northwest 20.8 to 48.7°C/km. We may have 110–153°C at a distance of 3–4 kilometres inside from the earth surface at the north-west. So the geothermal energy is a great opportunity for eliminating this electricity shortage.

Health care is one of the major aspects that most people in the world are deprived of. Recent advancement in microelectronic and information & communication technology mean that the measurement and transmission of physiological data for the disease monitoring and treatment purpose have become easier than ever. This paper proposed an IoT based end to end solution for ECG data acquisition, automated diagnosis of diseases and real-time monitoring of health condition. The system contains a smart ECG device embedded in a belt for data collection which is then transmitted to the cloud via Wi-Fi. This data can continuously be accessed by a remote healthcare server for classification and analysis purpose. The patient can access and monitor the ECG graph, Interval Analysis report, and HR measurement through the mobile application. The proposed system can additionally be used for real-time monitoring of Fetal Heart Rate (FHR) during gestation and diseases like Atrial Fibrillation. The system is hoped to dissolve distances between health care provider and patients, making medical services accessible from anywhere around the world.