Heartbeat detection from a hydraulic bed sensor using a clustering approach.

Encouraged by previous performance of a hydraulic bed sensor, this work presents a new hydraulic transducer configuration which improves the system's ability to capture a heartbeat signal from four subjects with different body weight and height, gender, age and cardiac history. It also proposes a new approach for detecting the occurrence of heartbeats from ballistocardiogram (BCG) signals through the use of the k-means clustering algorithm, based on finding the location of the J-peaks. Preliminary testing showed that the new transducer arrangement was able to capture the occurrence of heartbeats for all the participants, and the clustering approach achieved correct heartbeat detection ranging from 98.6 to 100% for three of them. Some considerations are discussed regarding adjustments that can be done in order to increase the correct detection of heartbeats for the participant whose percentage of correct detection ranged from 71.0 to 92.5%.

Refinement and evaluation of a hydraulic bed sensor.

Research indicates that long-term monitoring of vital signs and activity in elderly adults may provide opportunities for maintaining quality-of-life and extending independence into later years. Such a strategy requires development of a system to collect this data while imposing minimal intrusion into the lives of those being monitored. To further this goal, we have developed a hydraulic bed sensor to non-invasively monitor heartbeat and respiration during sleep. This paper describes the refinement of our developed prototype and signal processing methods, along with an evaluation of the robustness of our algorithms and results from testing. An evaluation of our sensor on a group of five diverse subjects (ranging in age from 24 to 67, two with cardiac history), in three different positions, demonstrates accuracy within 8 beats per minute up to 97.5% of the time.