An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm.

Aims: Ventricular tachycardia (VT) is a dangerous cardiac arrhythmia that can lead to sudden cardiac death. Early detection and management of VT is thus of high clinical importance. We hypothesize that it is possible to identify patients with VT during sinus rhythm by leveraging a continuous 24 h Holter electrocardiogram and artificial intelligence. Methods and results: We analysed a retrospective Holter data set from the Rambam Health Care Campus, Haifa, Israel, which included 1773 Holter recordings from 1570 non-VT patients and 52 recordings from 49 VT patients. Morphological and heart rate variability features were engineered from the raw electrocardiogram signal and fed, together with demographical features, to a data-driven model for the task of classifying a patient as either VT or non-VT. The model obtained an area under the receiving operative curve of 0.76 ± 0.07. Feature importance suggested that the proportion of premature ventricular beats and beat-to-beat interval variability was discriminative of VT, while demographic features were not. Conclusion: This original study demonstrates the feasibility of VT identification from sinus rhythm in Holter.

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. METHODS: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. RESULTS: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91-0.94 in RBDB and 0.93 in SHDB, compared to 0.89-0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.

OBJECTIVE: Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it is unclear whether the explicit or implicit claims made on DL superiority to the more classical feature engineering (FE) approaches, based on domain knowledge, hold. In addition, it remains unclear whether combining DL with FE may improve performance over a single modality. METHODS: To address these research gaps and in-line with recent major experiments, we revisited three tasks: cardiac arrhythmia diagnosis (multiclass-multilabel classification), atrial fibrillation risk prediction (binary classification), and age estimation (regression). We used an overall dataset of 2.3M 12-lead ECG recordings to train the following models for each task: i) a random forest taking FE as input; ii) an end-to-end DL model; and iii) a merged model of FE+DL. RESULTS: FE yielded comparable results to DL while necessitating significantly less data for the two classification tasks. DL outperformed FE for the regression task. For all tasks, merging FE with DL did not improve performance over DL alone. These findings were confirmed on the additional PTB-XL dataset. CONCLUSION: We found that for traditional 12-lead ECG based diagnosis tasks, DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task. We also found that combining FE with DL did not improve over DL alone, which suggests that the FE was redundant with the features learned by DL. SIGNIFICANCE: Our findings provides important recommendations on 12-lead ECG based machine learning strategy and data regime to choose for a given task. When looking at maximizing performance as the end goal, if the task is nontraditional and a large dataset is available then DL is preferable. If the task is a classical one and/or a small dataset is available then a FE approach may be the better choice.

Fast and Deterministic Fabrication of Sub-5 Nanometer Solid-State Pores by Feedback-Controlled Laser Processing.

Nanopores are single-molecule sensors capable of detecting and quantifying a broad range of unlabeled biomolecules including DNA and proteins. Nanopores' generic sensing principle has permitted the development of a vast range of biomolecular applications in genomics and proteomics, including single-molecule DNA sequencing and protein fingerprinting. Owing to their superior mechanical and electrical stability, many of the recent studies involved synthetic nanopores fabricated in thin solid-state membranes such as freestanding silicon nitride. However, to date, one of the bottlenecks in this field is the availability of a fast, reliable, and deterministic fabrication method capable of repeatedly forming small nanopores (i.e., sub 5 nm) in situ. Recently, it was demonstrated that a tightly focused laser beam can induce controlled etching of silicon nitride membranes suspended in buffered aqueous solutions. Herein, we demonstrate that nanopore laser drilling (LD) can produce nanopores deterministically to a prespecified size without user intervention. By optimizing the optical apparatus, and by designing a multistep control algorithm for the LD process, we demonstrate a fully automatic fabrication method for any user-defined nanopore size within minutes. The LD process results in a double bowl-shaped structure having a typical size of the laser point-spread function (PSF) at its openings. Numerical simulations of the characteristic LD nanopore shape provide conductance curves that fit the experimental result and support the idea that the pore is produced at the thinnest area formed by the back-to-back facings bowls. The presented LD fabrication method significantly enhances nanopore fabrication throughput and accuracy and hence can be adopted for a large range of biomolecular sensing applications.