AI-Assisted Echocardiographic Prescreening of Heart Failure With Preserved Ejection Fraction on the Basis of Intrabeat Dynamics.

OBJECTIVES: The aim of this study was to establish a rapid prescreening tool for heart failure with preserved ejection fraction (HFpEF) by using artificial intelligence (AI) techniques to detect abnormal echocardiographic patterns in structure and function on the basis of intrabeat dynamic changes in the left ventricle and the left atrium. BACKGROUND: Although diagnostic criteria for HFpEF have been established, rapid and accurate assessment of HFpEF using echocardiography remains challenging and highly desirable. METHODS: In total, 1,041 patients with HFpEF and 1,263 asymptomatic individuals were included in the study. The participants' 4-chamber view images were extracted from the echocardiographic files and randomly separated into training, validation, and internal testing data sets. An external testing data set comprising 150 patients with symptomatic chronic obstructive pulmonary disease and 315 patients with HFpEF from another hospital was used for further model validation. The intrabeat dynamics of the geometric measures were extracted frame by frame from the image sequence to train the AI models. RESULTS: The accuracy, sensitivity, and specificity of the best AI model for detecting HFpEF were 0.91, 0.96, and 0.85, respectively. The model was further validated using an external testing data set, and the accuracy, sensitivity, and specificity became 0.85, 0.79, and 0.89, respectively. The area under the receiver-operating characteristic curve was used to evaluate model classification ability. The highest area under the curve in the internal testing data set and external testing data set was 0.95. CONCLUSIONS: The AI system developed in this study, incorporating the novel concept of intrabeat dynamics, is a rapid, time-saving, and accurate prescreening method to facilitate HFpEF diagnosis. In addition to the classification of diagnostic outcomes, such an approach can automatically generate valuable quantitative metrics to assist clinicians in the diagnosis of HFpEF.

Electrocardiogram lead selection for intelligent screening of patients with systolic heart failure.

Electrocardiogram (ECG)-based intelligent screening for systolic heart failure (HF) is an emerging method that could become a low-cost and rapid screening tool for early diagnosis of the disease before the comprehensive echocardiographic procedure. We collected 12-lead ECG signals from 900 systolic HF patients (ejection fraction, EF < 50%) and 900 individuals with normal EF in the absence of HF symptoms. The 12-lead ECG signals were converted by continuous wavelet transform (CWT) to 2D spectra and classified using a 2D convolutional neural network (CNN). The 2D CWT spectra of 12-lead ECG signals were trained separately in 12 identical 2D-CNN models. The 12-lead classification results of the 2D-CNN model revealed that Lead V6 had the highest accuracy (0.93), sensitivity (0.97), specificity (0.89), and f1 scores (0.94) in the testing dataset. We designed four comprehensive scoring methods to integrate the 12-lead classification results into a key diagnostic index. The highest quality result among these four methods was obtained when Leads V5 and V6 of the 12-lead ECG signals were combined. Our new 12-lead ECG signal-based intelligent screening method using straightforward combination of ECG leads provides a fast and accurate approach for pre-screening for systolic HF.

Design and Construction of High-Frequency Cardiac Defibrillator for Small Animals.

The main goal of this research is to evaluate the defibrillation efficacy with the high-frequency waveform on ventricular fibrillation in small animals. A biphasic defibrillator with adjustable frequency was designed for this study. This custom-designed defibrillator can be adjusted to generate four different frequencies of 125, 250, 500, and 1000 Hz. Six rat hearts were induced VT/VF by electrical induction using the waveform of these four frequencies. Success VT/VF-induction by applying those four frequencies were recorded and observed by optical mapping. The results showed that the VT/VF-induction success rate is increasing along with higher frequencies. The VT/VF-induction success rate is 16% in 125Hz and 250 Hz, 33% in 500 Hz, and 100% in 1000 Hz with S1-S2 protocol at 100 ms coupling interval. Also, using optical mapping technique, shock-induced optical potential showed that only high-frequency waveform exhibited the largest tissue responses in the middle position of the heart. In conclusion, high-frequency (1000Hz) defibrillation waveform has the highest defibrillation efficacy comparing to other lower frequencies used in this study.

Effects of high-frequency biphasic shocks on ventricular vulnerability and defibrillation outcomes through synchronized virtual electrode responses.

Electrical defibrillation is a well-established treatment for cardiac dysrhythmias. Studies have suggested that shock-induced spatial sawtooth patterns and virtual electrodes are responsible for defibrillation efficacy. We hypothesize that high-frequency shocks enhance defibrillation efficacy by generating temporal sawtooth patterns and using rapid virtual electrodes synchronized with shock frequency. High-speed optical mapping was performed on isolated rat hearts at 2000 frames/s. Two defibrillation electrodes were placed on opposite sides of the ventricles. An S1-S2 pacing protocol was used to induce ventricular tachyarrhythmia (VTA). High-frequency shocks of equal energy but varying frequencies of 125-1000 Hz were used to evaluate VTA vulnerability and defibrillation success rate. The 1000-Hz shock had the highest VTA induction rate in the shorter S1-S2 intervals (50 and 100 ms) and the highest VTA defibrillation rate (70%) among all frequencies. Temporal sawtooth patterns and synchronous shock-induced virtual electrode responses could be observed with frequencies of up to 1000 Hz. The improved defibrillation outcome with high-frequency shocks suggests a lower energy requirement than that of low-frequency shocks for successful ventricular defibrillation.