# A Time-Frequency Domain Approach of Heart Rate Estimation From Photoplethysmographic (PPG) Signal

## TL;DR

This paper introduces a novel time-frequency domain method combining multi-stage adaptive filtering and spectral analysis to accurately estimate heart rate from PPG signals amidst motion artifacts, suitable for wearable devices.

## Contribution

It proposes a multi-stage adaptive filtering and spectral analysis approach that improves heart rate estimation accuracy over existing methods in wearable device scenarios.

## Key findings

- Average absolute error of 1.16 BPM
- Standard deviation of 1.74 BPM
- Better performance than TROIKA, JOSS, and SPECTRAP methods

## Abstract

Objective- Heart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various motion artifacts. The objective is to develop algorithms to reduce the effect of motion artifacts and thus obtain accurate heart rate estimation. Methods- Proposed heart rate estimation scheme utilizes both time and frequency domain analyses. Unlike conventional single stage adaptive filter, multi-stage cascaded adaptive filtering is introduced by using three channel accelerometer data to reduce the effect of motion artifacts. Both recursive least squares (RLS) and least mean squares (LMS) adaptive filters are tested. Moreover, singular spectrum analysis (SSA) is employed to obtain improved spectral peak tracking. The outputs from the filter block and SSA operation are logically combined and used for spectral domain heart rate estimation. Finally, a tracking algorithm is incorporated considering neighbouring estimates. Results- The proposed method provides an average absolute error of 1.16 beat per minute (BPM) with a standard deviation of 1.74 BPM while tested on publicly available database consisting of recordings from 12 subjects during physical activities. Conclusion- It is found that the proposed method provides consistently better heart rate estimation performance in comparison to that recently reported by TROIKA, JOSS and SPECTRAP methods. Significance- The proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose.