Abstract
In this thesis, a diagnostic grade mobile healthcare platform is designed to establish a mobile telecare environment for modem people. Although mHealth is not an innovative research topic, the improvements of wearable sensing technologies and today’s mobile devices can make such systems become more consummate and powerful than before. Additionally, the cooperation between physicians and engineers is another main driving force for these researches to become even more consummate in the past few years. In our platform, a wearable ECG recording prototype is developed to record single-lead (lead II) ECG signals of users. The whole prototype weight about 35 g (including an 850 mAh battery), and is only half of a credit card in size. With our well-developed and user-friendly smartphone application program, we can use a widespread mobile device as a hub to coordinate connections between wearable sensors and cloud techniques. Due to the computing power and the programability of today’s mobile phone, our hub can not only transmit recorded data onto medical cloud, but also provide several useful services and functions for users. This also is one of the main values of our platform. Although our prototype and application can provide such many functions, it is still amazingly energy efficient. For the sensor node prototype, an 850 mAh battery can make such device continuously recording and transmitting data for at least 24 hours (the total power consumption of the whole sensor is about 145 mW, while about 110 mW for the wireless bluetooth 2.1 module only). This is expected to be much longer after replacing the bluetooth module with the 4.0 Bluetooth low energy (BLE) version. For the mobile application, all service threads of the program are optimized to bring longest battery life to mobile devices. However, the actuarial battery life of the mobile hub depends on the specification of different devices. Generally, a quad-core Android phone with a 1800 mAh battery can have at least 6.5 hours battery life according to our experiments. Besides of this, high-end hardware is not required at our mobile hub. A general Android phone with 800 MHz single core CPU can run our program smoothly as well. Besides of the mobile hub, the patient-centric medical cloud is another priceless part of our mobile healthcare platform. Such cloud service can provide not only accurate detecting algorithms, but also advanced analyzing methods for biomedical signal processing. We designed an accurate algorithm to detect R-R intervals of recorded single-lead ECG signals using different operation method (DOM). Also, this algorithm is verified with MIT-BIH arrhythmia database, and the results show our algorithm has great performance. The sensitivity (Se%) and positive predictivity (+P%) of our DOM algorithm can achieve 98.80% and 99.19%, respectively. And if we ignore 3 worst cases of MIT-BIH database: 108, 203, 222, as many other researches do, we can even higher the value to 99.45% and 99.42%. In addition to this, many other ECG analyzing methods are also implemented in our platform. All of these methods were proved to be useful in different cardiac analysis. We also use the implemented analyzing methods and some time-frequency analysis methods in another innovative study. In this study, we intend to find the significant difference between heart rate variability (HRV) indices analyzed from non-alcoholic fatty liver decease (NAFLD) patients and normal people. This can help us define the influence of liver on heart in human body. Expectedly, results of this study shows that, NAFLD is associated with decreased ln sdNN and increased 0V percentage. Therefore, we declare that, further risk stratification of autonomic dysfunction with falls or cardiovascular diseases by these HRV parameters is required in patients with NAFLD.