Jo Woon Chong

PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application

Atrial fibrillation (AF) is a common and dangerous rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening.

Arrhythmia discrimination using a smart phone

We hypothesize that our smartphone-based arrhythmia discrimination algorithm with data acquisition approach reliably differentiates between normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs) in a diverse group of patients having these common arrhythmias. We combine root mean square of successive RR differences and Shannon entropy with Poincare plot (or turning point ratio method) and pulse rise and fall times to increase the sensitivity of AF discrimination and add new capabilities of PVC and PAC identification. To investigate the capability of the smartphone-based algorithm for arrhythmia discrimination, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion, as well as seven participants with PACs and four with PVCs were recruited. Using a smartphone, we collected 2-min pulsatile time series from each recruited subject. This clinical application results show that the proposed method detects NSR with specificity of 0.9886, and discriminates PVCs and PACs from AF with sensitivities of 0.9684 and 0.9783, respectively.

THE DETECTION AND DIFFERENTIATION OF ARRRHYTHMIAS USING A SMARTPHONE: A CLINICAL STUDY OF PATIENTS WITH ATRIAL FIBRILLATION, PREMATURE ATRIAL AND PREMATURE VENTRICULAR CONTRACTIONS

The detection of arrhythmias via a smartphone application would allow for timely detection and treatment of patients. This smartphone application has already been designed and used to successfully differentiate atrial fibrillation from normal sinus rhythm with an extremely high accuracy (97.6%), but

Motion and noise artifact-resilient atrial fibrillation detection algorithm for a smartphone

We have developed a motion and noise artifact (MNA)-resilient atrial fibrillation (AF) detection algorithm for smartphones that eliminates MNAs, and then detects AFs in smartphone camera recordings. MNA-corrupted episodes are observed to have larger values of turning point ratio (TPR), pulse slope, or Kurtosis compared to clean AF and normal sinus rhythm (NSR) episodes. On the other hand, AFs are shown to have larger root mean square of successive RR differences (RMSSD) and Shannon Entropy (ShE) [1]. Our developed AF algorithm is capable of separating MNAs, NSRs, AFs, which enhances the specificity of AF detection. We have recruited 88 subjects having AF at baseline and NSR after electrical cardioversion, and 11 subjects having MNA-corrupted NSRs to evaluate the performance of our AF algorithm. The clinical tests show that the proposed AF algorithm gives higher accuracy, sensitivity and specificity of 0.9667, 0.9765, 0.9714 compared to the previous AF algorithm [1].

Novel Fingertip Image-Based Heart Rate Detection Methods for a Smartphone

We hypothesize that our fingertip image-based heart rate detection methods using smartphone reliably detect the heart rhythm and rate of subjects. We propose fingertip curve line movement-based and fingertip image intensity-based detection methods, which both use the movement of successive fingertip images obtained from smartphone cameras. To investigate the performance of the proposed methods, heart rhythm and rate of the proposed methods are compared to those of the conventional method, which is based on average image pixel intensity. Using a smartphone, we collected 120 s pulsatile time series data from each recruited subject. The results show that the proposed fingertip curve line movement-based method detects heart rate with a maximum deviation of 0.0832 Hz and 0.124 Hz using time- and frequency-domain based estimation, respectively, compared to the conventional method. Moreover, another proposed fingertip image intensity-based method detects heart rate with a maximum deviation of 0.125 Hz and 0.03 Hz using time- and frequency-based estimation, respectively.

Motion and Noise Artifact-Resilient Atrial Fibrillation Detection Using a Smartphone

Smartphone signals corrupted by motion and noise artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.

A novel heart rate monitoring method using a smartphone

Accurate heart rate detection is important in healthcare and exercise monitoring. Recently, heart rate monitoring using a smartphone has been highlighted due to its convenience and accuracy. In this paper, we hypothesize that our smartphone-based heart rate detection algorithm reliably detects heart rate based on fingertip image changes. Here, we have used successive video camera fingertip images with edge detection and smoothing techniques to process the fingertip images and to find out the heart rate of the subject. To investigate the capability of our proposed algorithm, we recruited 3 subjects and collected 2-min video data from each subject. We evaluated the performance of our proposed method by comparing it to the previous average intensity-based method [1]. Test results show that our proposed and previous methods give similar heart rate detection performance.

An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone.

Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.