ABSTRACT
OBJECTIVES: We have developed a non.intrusive continuous PTT monitoring system, using a wearable device and wireless communication technology. METHODS: Pulse transit time (PTT) is a non.invasive measurement that shows promise in the continuous monitoring of blood pressure (BP) and the assessment of arterial stiffness. It has potential applications in wearable health monitoring devices. Generally, PTT is measured from the electrocardiogram (ECG) Rwave to a characteristic point on the peripheral pulse by photoplethysmography. However, for home healthcare applications, a system needs to be wearable and wireless. ECG and PPG were sampled at 1200 Hz and transmitted to a personal computer (PC) using Bluetooth communications. Heart rate (HR) and PTT were calculated by the PC from the signals received and waveforms of ECG, PPG, PTT and HR were displayed. RESULTS: In this study, we implemented a system that could be adapted to the wrist and measured ECG and a Photoplethysmogram (PPG). CONCLUSIONS: The wearable continuous PTT monitoring system developed in this study could be useful in home cardiovascular healthcare.
Subject(s)
Blood Pressure , Delivery of Health Care , Electrocardiography , Heart Rate , Microcomputers , Photoplethysmography , Pulse Wave Analysis , Vascular Stiffness , WristABSTRACT
In this paper, we studied a signal processing method which extracts each MUAP(motor unit action potential) from EMG(Electromyogram) interference pattern for clinical diagnostic purposes. First of all, differential digital filtering is selected for eliminating the spike components of the MUAP' s from the backgroud noise. And the algorithm identifies the spikes over the certanin threshold by template matching in frequency domain. After missing or false firing factor is cut off at the IPJ(inter pulse interval) histogram, we average the MUAP waveforms from the raw signal using the identified spikes as triggers, and finally, measure their amplitudes, durations, and numbers of phases. Specially, we introduce algorithm performed by template matching in the frequency domain. This algorithm performed as follows. A typical 3 second signal recorded from the biceps brachii muscle using a conventional needle electrode during a isometric contraction. And decomposed active MUAP' s decomposed from original EMG signal.