Feasibility and Reliability of Smartwatch to Obtain Precordial Lead Electrocardiogram Recordings

The value of circadian heart rate variability for the estimation of obstructive sleep apnea severity in adult males

OBJECTIVES

Heart rate variability (HRV) is becoming more prevalent as a measurable parameter in wearable sleep-monitoring devices, which are simple and effective instruments for illness evaluation. Currently, most studies on investigating OSA severity and HRV have measured heart rates during wakefulness or sleep. Therefore, the objective of this study was to investigate the circadian rhythm of HRV in male patients with OSA and its value for the estimation of OSA severity using group-based trajectory modeling.

METHODS

Patients with complaints of snoring were enrolled from the Sleep Center of Shandong Qianfoshan Hospital. Patients were divided into 3 groups according to apnea hypopnea index (AHI in events/h), as follows: (<15, 15≤AHI<30, and ≥30). HRV parameters were calculated using 24 h Holter monitoring, which included time-domain and frequency-domain indices. Circadian differences in the standard deviation of normal to normal (SDNN) were evaluated for OSA severity using analysis of variance, trajectory analysis, and multinomial logistic regression.

RESULTS

A total of 228 patients were enrolled, 47 with mild OSA, 48 moderate, and 133 severe. Patients with severe OSA exhibited reduced triangular index and higher very low frequency than those in the other groups. Circadian HRV showed that nocturnal SDNN was considerably higher than daytime SDNN in patients with severe OSA. The difference among the OSA groups was significant at 23, 24, 2, and 3 o'clock sharp between the severe and moderate OSA groups (all P<0.05). The heterogeneity of circadian HRV trajectories in OSA was strongly associated with OSA severity, including sleep structure and hypoxia-related parameters. Among the low-to-low, low-to-high, high-to-low, and high-to-high groups, OSA severity in the low-to-high group was the most severe, especially compared with the low-to-low and high-to-low SDNN groups, respectively.

CONCLUSIONS

Circadian HRV in patients with OSA emerged as low daytime and high nocturnal in SDNN, particularly in men with severe OSA. The heterogeneity of circadian HRV revealed that trajectories with low daytime and significantly high nighttime were more strongly associated with severe OSA. Thus, circadian HRV trajectories may be useful to identify the severity of OSA.

The use of smartwatch electrocardiogram beyond arrhythmia detection

The adoption of wearables in medicine has expanded worldwide with a rapidly growing number of consumers and new features capable of real-time monitoring of health parameters such as the ability to record and transmit a single-lead electrocardiogram (ECG). Smartwatch ECGs are increasingly used but current smartwatches only screen for atrial fibrillation (AF). Most of the literature has focused on analyzing the smartwatch ECG accuracy for the detection of AF or other tachycardias. As with the conventional ECG, this tool may be used for many more purposes than only detection of AF. The objectives of this review are to describe the published literature regarding the accuracy and clinical value of recording a smartwatch ECG in other situations than diagnosis of tachycardia and discuss possible techniques to optimize the diagnostic yield.

The Reliability of the Apple Watch's Electrocardiogram

Background An electrocardiogram (ECG) is a standard tool used to detect various cardiovascular abnormalities. Detection sensitivity for atrial fibrillation (AF) was recently shown to be greatly increased by using short, intermittent ECG recordings. Modern mobile ECG recording devices that can monitor patients' heart activities around the clock have made this a reality. The Apple Watch is one of these portable ECG devices that can detect heart rhythms and is approved by the American FDA for screening and detecting AF. Objectives We compared the results of the Apple Watch I lead ECG with conventional ECG results to assess the sensitivity and specificity of the Apple Watch I lead ECG. We then determined the abnormalities that can be detected by the Apple Watch I lead ECG. Methods This study was conducted on outpatient cardiac clinics at King Abdullah bin Abdulaziz University Hospital (KAAUH), and Prince Sultan Cardiac Center (PSCC), from May to October 2021. A standard 12-lead ECG was recorded and compared with the Apple Watch I lead ECG. A total of 469 ECG comparisons were included in this study and evaluated by two investigators. The data on patient demographics, medical and medication history, and ECG data were reviewed and analyzed using IBM SPSS Statistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). Results No significant differences were seen between the Apple Watch and the 12-lead ECG in terms of the studied ECG characteristics. A significant and strong positive correlation between the heart rate measurements in the 12-lead ECG and Apple Watch ECG was documented. The most commonly found abnormalities in the Apple Watch ECG were AF in 37 (7.9%), followed by first-degree atrioventricular (AV) block in 32 (6.8%). The sensitivity of Apple Watch's automated interpretation to detect an AF was 99.54%, while the manual interpretation yielded a sensitivity of 100%. Conclusion The results of this study demonstrated a robust relationship between the 12-lead ECG and Apple Watch ECG in the diagnosis of arrhythmias. Consequently, cardiac patients may consider the Apple Watch ECG a trustworthy remote monitoring technique.

Watch for tachycardia

Background

Wearable devices capable of measuring health metrics are becoming increasingly prevalent. Most work has investigated the potential for these devices in the context of atrial fibrillation, our case highlights the potential of wearable devices across a wider range of arrhythmia.

Case presentation

A 51-year-old woman was referred to the cardiology clinic for an assessment of symptoms of intermittent exertional shortness of breath and palpitation. The patient was otherwise fit and well, took limited alcohol and no caffeine, and was a never smoker. There was no family history of heart disease. Physical examination in clinic was unremarkable, and a 12-lead electrocardiogram (ECG), seven-day ambulatory ECG, exercise stress ECG, and trans-thoracic echocardiogram were all normal. During a severe episode the patient recorded an ECG using an Apple Watch (Apple Inc, California, USA). This was forwarded to the patient’s cardiologist, who suspected a broad complex tachycardia and organised an urgent follow-up appointment. A further 72-h Holter ECG monitor showed frequent sustained periods of monomorphic ventricular tachycardia, confirming the watch findings. The patient was started on beta blocker therapy with a rapid improvement in symptoms.

Conclusions

Current smartwatch technology can reliably identify irregular rhythms and can distinguish atrial fibrillation from sinus rhythm, with emerging evidence supporting detection of other cardiovascular diseases, including medical emergencies. There may also be a role for wearable devices in screening young populations for predictors of sudden cardiac death. At present device outputs require clinician interpretation, but in the future patients may present to primary or secondary care with a firm diagnosis of arrhythmia and may already be making wearable device guided behaviour changes.