Real-Time Intelligent Healthcare Monitoring and Diagnosis System Through Deep Learning and Segmented Analysis

Medical facilities and technologies have been greatly improved through the application of biosensors, healthcare systems, health diagnosis and disease prevention technologies. However, wireless transmission and deep learning neural network are essential applications and new methods in biomedical engineering nowadays. Hence, authors established a new real-time and intelligent healthcare system that will help the physician’s diagnosis over the patient’s condition and will have a great contribution to medical research. Physiological conditions can be monitored and primary diagnosis will be determined which will help people primarily for personal health care. This paper focused on the collection, transmission, and analysis of physiological signals captured from biosensors with the application of deep learning and segmented analysis for the prediction of heart diseases. Biosensors employed were non-invasive composed of infrared body temperature sensor (MLX90614), heart rate and blood oxygen sensor (MAX30100) and ECG sensor (AD8232). This research used these biosensors to collect signals integrated with Arduino UNO as a central module to process and analyze those signals. ESP8266 Wi-Fi microchip was used to transmit digitized result signals to the database for deep learning analysis. The first segment of deep learning analysis is the Long-Short Term Memory (LSTM) network applied for the temperature, heart rate and arterial oxygen saturation prediction. A rolled training technique was used to provide accurate predictions in this segment. The second segment used was the Convolutional Neural Network (CNN), which comprises three hidden layers to analyze the ECG signals from the image datasets. Deep learning tools used were the powerful python language, python based Anaconda, Google’s TensorFlow and open source neural network library Keras. The algorithm was used for evaluation using the available MIT-BIH ECG database from Physionet databases which attained 99.05% accuracy and arrived at only 4.96% loss rate after 30 training steps. The implementation of the system is comprised of physiological parameter sensing system, the wireless transmission system and the deep neural network prediction system. User interfaces were also developed such as the LCD display which shows values of body temperature, heart rate and arterial oxygen saturation level. Web page and app were created to allow users or doctors for visual presentations of the results of analysis. The webpage contains information about the system, deep learning networks used, biosensors and the historical graph of about the patient’s body temperature, heart rate and oxygen saturation. It also indicates the normal ranges of the physiological parameters.