Precision wearable accelerometer contact microphones for longitudinal monitoring of mechano-acoustic cardiopulmonary signals

Abstract

Mechano-acoustic signals emanating from the heart and lungs contain valuable information about the cardiopulmonary system. Unobtrusive wearable sensors capable of monitoring these signals longitudinally can detect early pathological signatures and titrate care accordingly. Here, we present a wearable, hermetically-sealed high-precision vibration sensor that combines the characteristics of an accelerometer and a contact microphone to acquire wideband mechano-acoustic physiological signals, and enable simultaneous monitoring of multiple health factors associated with the cardiopulmonary system including heart and respiratory rate, heart sounds, lung sounds, and body motion and position of an individual. The encapsulated accelerometer contact microphone (ACM) utilizes nano-gap transducers to achieve extraordinary sensitivity in a wide bandwidth (DC-12 kHz) with high dynamic range. The sensors were used to obtain health factors of six control subjects with varying body mass index, and their feasibility in detection of weak mechano-acoustic signals such as pathological heart sounds and shallow breathing patterns is evaluated on patients with preexisting conditions.

Keywords: Biomedical engineering; Electrical and electronic engineering; Health care.

Fig. 1. Hermetically-sealed sensor with nanogaps for cardiopulmonary health monitoring.

a conceptual representation of encapsulated sensor positioned on the on the chest wall (blue circle) to simultaneously monitor heart rate, heart sounds, respiratory rate, breath sounds along with body motion and position. The exploded view displays the fabricated microsensor (2 × 2 × 1 mm) and its cross-sectional view showcasing enabling technology of high aspect-ratio (>150), ultra-thin 270 nm capacitive gap, b SEM of uncapped accelerometer contact microphone (ACM) device. The proof mass anchored on the side using torsional tethers c COMSOL Multiphysics simulation illustrating the operational mode shape of the sensor and showcasing the location of torsional tethers and sense electrodes d transducer response to normally applied acceleration with measured sensitivity of 76 mV/g and cross-axis sensitivity lower than 3%, e Allan deviation plot exhibiting low-noise performance of 127 µg/√Hz.

Fig. 2. Recording cardiopulmonary vibrations, sounds and body motion.

a Time domain plot of measured SCG signal. Peaks corresponding to occurrence of closing of mitral valve (MC), opening of aortic valve (AO), closing of aortic valve (AC) and opening of mitral valve (MO) are indicated. b Recorded waveforms of two cardiac cycles showcasing sensitivity to the two major cardiac sounds (S₁ and S₂). Time intervals of inter-beat, systole and diastole are specified. c Sensor output signal representing motion of the chest wall during deep-breathing respiratory cycles. Time intervals of inhalation and exhalation are identified for computation of respiratory rate. d High frequency lung sounds of inhalation and exhalation as recorded by the vibration microsensor. e Body motion tracking in three dimensions, using the ACM along with two in-plane accelerometers, as the individual performs side-to-side (orange) and frontal (green) bending exercises. Time domain plots recorded during exercising showcasing the wide dynamic range of the sensor.

Fig. 3. Comparison and computation of diagnostic parameters.

a Time domain plot of measured ECG signal alongside the ACM. b Correlation plot illustrating linear curve fit with r² = 0.98 confirming effective use of ACM for computation of diagnostic parameters. c Bland-Altman plot comparing the technologies demonstrates 95% confidence interval having a range of 0.01 s.

Fig. 4. Validating detection of weak mechano-acoustic signals.

a Time domain plot of measured cardiac sounds. Presence of abnormal ventricular gallop (S₃) occurring 150 ms after S₂ heart sound. b Breathing pattern monitored by the ACM shows instances of shallow breathing indicating shortness of breath. Respiratory rate of 15 breaths per minute is calculated based on periodicity of the signal.