Shortness of breath and difficulty in breathing are directly associated with deteriorating conditions in patients with heart failure (HF) and/or chroni obstructive pulmonary disease (COPD). Continuous monitoring of respiratory status in these patients can alert caregivers to administer early interventions to manage disease symptoms, thus preventing catastrophic events and improving quality of life. Unfortunately, continuous monitoring of respiratory rate and pattern is often overlooked or neglected in clinical practice due to the general difficulty in performing these measurements, especially in non-intubated ambulatory settings. Present measurement methods that capture the patient's airway are most accurate but difficult to administer and often intolerable for the patient, whereas methods that rely on capturing chest motion historically suffer from poor accuracy due to motion artifacts. We propose a MIMO-based motion artifact cancelling multi-lead impedance measurement method for robust ambulatory respiratory rate monitoring. We have recently demonstrated that respiration and motion artifacts are theoretically separable in ambulatory subjects with multi-lead impedance measurements. The motion artifacts are sporadic and localized high energy non-stationary events compared to respiratory signals. Due to the limited dynamic range and quantization errors of the conventional data acquisition system, source separation algorithms struggle to extract reliable respiratory signal information. The proposed MIMO system approach overcomes these limitations by integrating statistical learning directly within analog to digital conversion process. As a result, this miniaturized hardware realization method enables continuous, real-time and power-efficient tracking of the respiratory signal corrupted by motion artifacts. Preliminary study of the MIMO based algorithm has shown promising results for ambulatory respiratory rate monitoring. In this research we plan to develop the optimized hardware prototype of the MIMO system to validate the performance under real ambulatory conditions. We believe that the MIMO based multi-lead imped- ance measurement method is likely to be accepted by both clinicians and patients due to routine use of electrodes. Going forward, in our future work we envision integrating the proposed research with ECG monitoring sharing same set of electrodes to provide continuous and accurate cardiorespiratory information for general ward as well as for home health/wellness monitoring of ambulatory patients.
Continuous monitoring of respiratory status in COPD and HF patients can alert caregivers to administer early interventions to manage disease symptoms, thus preventing catastrophic events and improving quality of life. In ambulatory patients, accuracy remains an issue for non-intubated methods relying on chest motion due to physical motion artifacts. We propose to develop and validate MIMO based motion-artifact cancelling multi-lead impedance measurement technique for robust ambulatory respiratory rate monitoring.