Wrist band photoplethysmography in detection of individual pulses in atrial fibrillation and algorithm-based detection of atrial fibrillation

AIMS

Atrial fibrillation (AF) is the most common tachyarrhythmia and a significant cause of cardioembolic strokes. Atrial fibrillation is often intermittent and asymptomatic making detection a major clinical challenge. We evaluated a photoplethysmography (PPG) wrist band in individual pulse detection in patients with AF and tested the reliability of two commonly used algorithms for AF detection.

METHODS AND RESULTS

A 5-min PPG was recorded from patients with AF or sinus rhythm (SR) with a wrist band and analysed with two AF detection algorithms; AFEvidence and COSEn. Simultaneously registered electrocardiogram served as the golden standard for rhythm analysis and was interpreted by two cardiologists. The study population consisted of 213 (106 AF, 107 SR) patients. The wrist band PPG achieved individual pulse detection with a sensitivity of 91.7 ± 11.2% and a positive predictive value (PPV) of 97.5 ± 4.6% for AF, with a sensitivity of 99.4 ± 1.5% [7.7% (95% confidence interval, 95% CI 5.5% to 9.9%); P < 0.001] and PPV of 98.1 ± 4.1% [0.6% (95% CI -0.6% to 1.7%); P = 0.350] for SR. The pulse detection sensitivity was lower 86.7 ± 13.9% with recent-onset AF (AF duration <48 h, n = 43, 40.6%) as compared to late AF (≥48 h, n = 63, 59.4%) with 95.1 ± 7.2% [-8.3% (95% CI -12.9% to -3.7%); P = 0.001]. For the detection of AF from the wrist band PPG, the sensitivities were 96.2%/95.3% and specificity 98.1% with two algorithms.

CONCLUSION

The wrist band PPG enabled accurate algorithm-based detection of AF with two AF detection algorithms and high individual pulse detection. Algorithms allowed accurate detection of AF from the PPG. A PPG wrist band provides an easy solution for AF screening.

224Wireless ECG embedded in a necklace enables reliable detection of atrial fibrillation

Funding Acknowledgements

Goverment grant, Heart2save Ltd cooperation.

Background/Introduction:

Atrial fibrillation (AF) is the most common arrhythmia and a fast-growing public health problem worldwide. Main and the most severe complication of AF is an embolic stroke. Approximately 25% of all strokes are caused by AF. AF is often symptomless and intermittent, making its detection and diagnosing challenging. Many AF patients have both symptomatic and asymptomatic periods of arrhythmia. In addition, during the early stage of the disease, AF is often paroxysmal making the diagnosis of AF a major clinical challenge.

Purpose

Wearable ECG devices could enable repetitive rhythm monitoring over a long term and thus improve the probability of AF detection. Wearable devices have the advantage of being easily available. However, they also need to be reliable and their design should appeal customers. Our aim was to study the suitability and reliability of novel measurement technique (necklace-ECG, Figure1) for the diagnosis of AF.

Methods

The study population consisted of 145 adult volunteers at Kuopio University Hospital emergency department (66 AF and 79 sinus rhythm (SR), diagnosed from continuous 3-lead ECG recording. All study subjects performed a thirty-second self-performed ECG recording with a single-lead necklace-embedded ECG recorder (necklace-ECG) keeping the recorder between palms of their hands. The ECG recordings were analyzed with automatic AF detection algorithm. Two cardiologists interpreted independently in blinded fashion the necklace-ECG recordings and simultaneously registered 3-lead ECGs which served as golden standard for the final rhythm diagnosis.

Results

Necklace-ECG produced an interpretable ECG recording in 91.0%/86.2% patients (Doc1/Doc2, respectively). Additionally, 93.1% of ECG recordings were interpretable by the automatic analysis service (98.7% in SR patients and 86.4% of AF patients).

Base on the necklace ECG recording, cardiologists were able to diagnose AF with sensitivity of 98.2%/96.3% (Doc1/Doc2, respectively) and specificity of 100% (Doc1 and Doc2, respectively). The automatic arrhythmia algorithm diagnosed AF with sensitivity of 94.7% and specificity of 100%.

Conclusions

The necklace-ECG device produces ECG recording with sufficient quality for the detection of AF with good sensitivity and specificity as evaluated both by cardiologist and automated AF-detection algorithm. Thus, wearable necklace-ECG provides a new and easy method for screening, identifying and diagnosing AF.

Abstract Figure 1

Dynamic modeling of respiratory sinus arrhythmia component from HRV with multivariate Kalman smoother

The estimates of heart rate variability (HRV) low frequency (LF) and high frequency (HF) components with constant frequency bands may distort when the frequency of respiratory sinus arrhythmia induced HF component approaches the LF-HF frequency limit. In this study we present a method for dynamically estimating the LF-HF limit and dividing the spectrum to LF and HF components that can overlap. The method is based on multivariate autoregressive model which is solved dynamically with Kalman smoother algorithm. The spectra of each individual pole with all the zeros are calculated and then multiplied with a Hanning window on the pole frequency. These spectra are summed to LF or HF components. The method was applied to three subjects whose electrocardiogram and respiration was recorded during a controlled breathing protocol. The results show that the HF component power increases when breathing frequency decreases. Also the component powers obtained with the presented method are reliable even when LF and HF frequencies are close to each other.

Hypoglycemia detection based on cardiac repolarization features

Hypoglycemia is known to affect repolarization characteristics of the heart. These changes are shown from ECG by prolonged QT-time and T-wave flattening. In this study we constructed a classifier based on these ECG parameters. By using the classifier we tried to detect hypoglycemic events from measurements of 22 test subjects. Hypoglycemic state was achieved using glucose clamp technique. Used test protocol consisted of three stages: normoglycemic period, transition period (blood glucose concentration decreasing) and hypoglycemic period. Subjects were divided into three groups: 9 healthy controls (Healthy), 6 otherwise healthy type 1 diabetics (T1DM) and 7 type 1 diabetics with disease complications (T1DMc). Detection of hypoglycemic event could be made passably from 15/22 measurements. In addition, we found that detection process is easier for healthy and T1DM groups than T1DMc group diabetics because in T1DMc group subjects' have lower autonomic response to hypoglycemic events. Also we noticed that changes in ECG occurs few minutes after blood glucose is decreased below 3.5 mmol/1.

Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects

Hypoglycemia is known to affect the repolarization characteristics of the heart, but the mechanisms behind these changes are not completely understood. We analyzed repolarization characteristics continuously from 22 subjects during normoglycemic period, transition period (blood glucose concentration decreasing) and hypoglycemic period from nine healthy controls (Healthy), six otherwise healthy type 1 diabetics (T1DM) and seven type 1 diabetics with disease complications (T1DMc). An advanced principal component regression (PCR)-based method was used for estimating ECG parameters beat-by-beat, and thus, continuous comparison between the repolarization characteristics and blood glucose values was made. We observed that hypoglycemia related ECG changes in the T1DMc group were smaller than changes in the Healthy and T1DM groups. We also noticed that when glucose concentration remained at a low level, the heart rate corrected QT interval prolonged progressively. Finally, a few minutes time lag was observed between the start of hypoglycemia and cardiac repolarization changes. One explanation for these observations could be that hypoglycemia related hormonal changes have a significant role behind the repolarization changes. This could explain at least the observed time lag (hormonal changes are slow) and the lower repolarization changes in the T1DMc group (hormonal secretion lowered in long duration diabetics).

Analysis of heart rate variability dynamics during propofol and dexmedetomidine anesthesia

It has been observed that heart rate variability (HRV) diminishes during anesthesia, but the exact mechanisms causing it are not completely understood. The aim of this paper was to study the dynamics of HRV during low dose propofol (N=9) and dexmedetomidine (N=8) anesthesia by using state-of-the-art time-varying methods, and thereby ultimately try to improve the safety of anesthesia. The time-varying spectrum is estimated by using a Kalman smoother approach. The results show that there is an overall increase in HRV and decrease in heart rate prior to loss of consciousness. For dexmedetomidine these changes are more considerable than for propofol. For dexmedetomidine the variability also seems to start decreasing right after loss of consciousness, whereas for propofol HRV continues increasing.

Time-varying spectrum estimation of heart rate variability signals with Kalman smoother algorithm

A time-varying parametric spectrum estimation method for analyzing dynamics of heart rate variability (HRV) signals is presented. In the method, HRV signal is first modeled with a time-varying autoregressive model and the model parameters are solved recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated model parameters. The obtained spectrum can be further decomposed into separate components, which is especially advantageous in HRV applications where low frequency (LF) and high frequency (HF) components are generally aimed to be distinguished. As case studies, the dynamics of HRV signals recorded during 1) orthostatic test, 2) exercise test and 3) simulated driving task are analyzed.