Artificial Intelligence in the Diagnosis and Management of Arrhythmias

Venkat D. Nagarajan; Su-Lin Lee; Jan-Lukas Robertus; Christoph A. Nienaber; Natalia A. Trayanova; Sabine Ernst

Disclosures

Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Arrhythmia Detection Using Artificial Intelligence

Since digitalization of ECG, AI methods have been employed in computerized interpretation of ECGs. Whilst ML methods revealed high sensitivity and specificity for detecting normal sinus rhythm, their abilities were lower than expert cardiologists for the identification of cardiac arrhythmias.[5] One of the main deterrents have been the presence of noise, small or varying P waves resulting in over diagnosis of atrial fibrillation (AF), paced rhythms, poor-quality ECGs, tremor, and previously untrained rhythms. With better ML algorithms, noise reduction techniques and advanced feature extraction, selection and reduction methods [including use of the unsupervised deep neural network (DNN)], computerized interpretation of ECGs has clearly improved arrhythmia detection achieving an accuracy close to 95%.[6,7]

Hannun et al.[8] developed an end to end deep learning (DL) approach for ECG analysis by using a DNN for identifying 12 rhythm abnormalities by using 91 232 single-lead ECGs. When validated against independent data reported by a committee of certified cardiologists, their algorithm was shown to be superior to an average cardiologist in identifying these rhythm abnormalities (ROC 0.97 vs. 0.78).

With the development of unsupervised DNN algorithms, more interest has been generated amongst researchers for identifying hidden diseased state signatures in a 12-lead ECG. So far, it has been shown to be feasible to detect hyperkalaemia,[9] heart failure,[10] hypoglycaemia,[11] and even changes in emotional states[12] using 12-lead ECG. Attia et al.[13] at Mayo Clinic Rochester assessed the feasibility of identifying previous episodes of or impending AF using an AI-enabled 12-lead ECG in normal sinus rhythm. They used 0.65 million ECGs to train, validate, and test the AI algorithm in a 7:1:2 ratio and found that AI-enabled ECG recorded during normal sinus rhythm performed well as a screening test to identify AF with an accuracy of 79.4% and this improved to 83.4% when it was a first ECG following an episode of AF. Additional multiple ECGs improved the model accuracy leading to the hypothesis that structural changes that may precede AF including myocyte hypertrophy, fibrosis, or chamber dilatation may result in subtle multifaceted changes in ECG that may otherwise be unrecognized by the human eye but are detectable by a DNN. This observation has important clinical implications with potential point-of-care identification of individuals at the risk of AF and for the embolic stroke of undetermined source.

Deep neural network has also been used to predict hypertrophic cardiomyopathy (HCM),[14] age and sex,[15] and plasma dofetilide concentration from ECG.[16]

Advancements in sensor technology, telecommunications (increased availability of wireless, Wi-Fi, Bluetooth and smartphone technologies), availability of web-based data storage, and AI-aided analysis have seen rapid growth of handheld and wearable cardiac monitoring systems (Table 3).

Whilst several handheld devices are available, the AliveCor Heart Monitor, an ECG-based system, has been studied extensively for AF detection in symptomatic patients.[17,23–27] In most of these studies, AliveCor Heart Monitor achieved well over 90% sensitivity and specificity for AF detection both in outpatient and hospital settings.[28] In a comparison with traditional transtelephonic monitor, AliveCor Heart Monitor achieved 100% sensitivity and 97% specificity for AF and atrial flutter detection.[29] Similar results were seen with the use of PPG-based systems coupled with AI-aided analysis.[30,31] In the WATCH AF trial, Dörr et al.[22] studied the efficacy of a smartphone PPG-based algorithm in AF detection. The PPG algorithm achieved a sensitivity of 93.7%, a specificity of 98.2%, and an accuracy of 96.1% to detect AF.

In a recent community-based trial on the utility of a smartwatch in AF detection (Apple Heart Study),[19] smartwatch in conjunction with a pulse notification algorithm showed promising results in 0.41 million participants with no prior history of AF. The smartwatch application collected single 1-min tachograms every 2 h. If a smartwatch-based irregular pulse notification algorithm identified possible AF, the participant was notified to have further simultaneous 7-day monitoring using an ECG patch. The smartwatch-based algorithm had a positive predictive value of 0.84 (95% confidence interval 0.76–0.92) for identifying AF during the simultaneous monitoring period. Similar results were shown in yet another population-based AF screening study using a PPG algorithm in conjunction with smart devices.[18]

Chen et al.[20] studied AF detection using a smart wristband equipped with both ECG and PPG sensors, comparing ECG and PPG individually as well as in combination. They demonstrated higher accuracy of 97.5% for AF detection with the combination against 94.7% and 93.2% with ECG and PPG, respectively.

Wasserlauf et al.[21] compared smartwatch detection of AF using a DL algorithm (episodes lasting ≥1 h) with an insertable loop recorder. They analysed 31 348 h of simultaneously recorded data. The smartwatch algorithm achieved 97.5% and 97.7% for episode sensitivity and duration sensitivity, respectively.

Artificial intelligence-enabled monitoring systems are affordable and reliable, can be used for continuous ambulatory monitoring, and will facilitate the detection of vulnerable groups.[32] A step closer to the holy grail of early AF detection within the community in otherwise asymptomatic patients may in fact initiate a paradigm shift in arrhythmia detection.

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