Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients

Abstract

Wearable home-monitoring devices acquiring various biosignals such as the electrocardiogram, photoplethysmogram, electromyogram, respirational activity and movements have become popular in many fields of research, medical diagnostics and commercial applications. Especially ambulatory settings introduce still unsolved challenges to the development of sensor hardware and smart signal processing approaches. This work gives a detailed insight into a novel wireless body sensor network and addresses critical aspects such as signal quality, synchronicity among multiple devices as well as the system's overall capabilities and limitations in cardiovascular monitoring. An early sign of typical cardiovascular diseases is often shown by disturbed autonomic regulations such as orthostatic intolerance. In that context, blood pressure measurements play an important role to observe abnormalities like hypo- or hypertensions. Non-invasive and unobtrusive blood pressure monitoring still poses a significant challenge, promoting alternative approaches including pulse wave velocity considerations. In the scope of this work, the presented hardware is applied to demonstrate the continuous extraction of multi modal parameters like pulse arrival time within a preliminary clinical study. A Schellong test to diagnose orthostatic hypotension which is typically based on blood pressure cuff measurements has been conducted, serving as an application that might significantly benefit from novel multi-modal measurement principles. It is further shown that the system's synchronicity is as precise as 30 μs and that the integrated analog preprocessing circuits and additional accelerometer data provide significant advantages in ambulatory measurement environments.

A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors

“…Wireless and unobtrusive devices are favored, especially when it comes to unsupervised long-time measurements. The measurements conducted in this work have been acquired using a Bluetooth synchronized body sensor network (BSN) whose technical details were published in [ 23 ]. This architecture provides a modular platform giving way to an easy integration of multiple sensors.…”

“…Figure 2 depicts the assembled hardware sensors of the deployed system. All signals are sampled at 500 Hz corresponding to a temporal resolution of 2 ms, which comes with a synchronization accuracy of 30 s [ 23 ].…”

Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features

“…Apart from the reference ABP measurement device, all hardware is part of the in-house, custom-built robust Body Sensor Network (rBSN) [3] of the EMSP Chair at the TU Berlin. All signals are acquired at a sampling rate of 500 samples/s with 12 bits of resolution, and are guaranteed by design to be synchronous within 100 µs.…”

Pulse Compression Shape-Based ADC/DAC Chain Synchronization Measurement Algorithm with Sub-Sampling Resolution

“…This approach relies on an external synchronization source, and the limited performance of the microcontroller restricts the synchronization accuracy. To meet the synchronization requirements of current high-performance systems, research efforts have been focused on internal design aspects [23][24][25]. Scholars have proposed a daisy chain structure that utilizes the internal frequency division system of high-speed converters to achieve synchronization through the propagation of the synchronization signal [26].…”