Computer-aided reading of 12-lead scalar ECGs was performed in 2.883 out-patients with a tracing- and processing-system of IBM, Periphere Computer Systeme and Schwarzer using the Bonner analysis-program version 2. After analog-to-digital conversion and storage on a floppy disk the ECG data were transferred by telephone to an IBM-computer at Munich. It the same way the prints were received from there. The review by a cardiologist showed correct interpretation in 95%. The accuracy was calculated as follows: Sensitivity 96%, specificity 96%. The same program can now be used in an ECG-cart with a built in microprocessor, a so-called stand-alone version.

Download full-text PDF

Source

Publication Analysis

Top Keywords

[computer-assisted ecg
4
ecg evaluation
4
evaluation polyclinic
4
polyclinic patients]
4
patients] computer-aided
4
computer-aided reading
4
reading 12-lead
4
12-lead scalar
4
scalar ecgs
4
ecgs performed
4

Similar Publications

Background: Hypertension is a leading cause of cardiovascular disease and premature death worldwide, and it puts a heavy burden on the healthcare system. Therefore, it is very important to detect and evaluate hypertension and related cardiovascular events to enable early prevention, detection, and management. Hypertension can be detected in a timely manner with cardiac signals, such as through an electrocardiogram (ECG) and photoplethysmogram (PPG) , which can be observed via wearable sensors.

View Article and Find Full Text PDF

A Deep Learning Approach for Mental Fatigue State Assessment.

Sensors (Basel)

January 2025

Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.

View Article and Find Full Text PDF

Generally, the electrocardiography (ECG) system plays an important role in preventing and diagnosing heart diseases. To further improve the amenity and convenience of using an ECG system, we built a customized capacitive electrocardiography (cECG) system with one wet electrode, sixteen non-contact electrodes, two ADS1299 chips, and one STM32F303-based microcontroller unit (MCU). This new cECG system could acquire, save, and display the ECG data in real time.

View Article and Find Full Text PDF

Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations.

Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children.

View Article and Find Full Text PDF

A knowledge embedded multimodal pseudo-siamese model for atrial fibrillation detection.

Sci Rep

January 2025

School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102, People's Republic of China.

Atrial fibrillation (AF) is a common arrhythmia disease with a higher incidence rate. The diagnosis of AF is time-consuming. Although many ECG classification models have been proposed to assist in AF detection, they are prone to misclassifying indistinguishable noise signals, and the context information of long-term signals is also ignored, which impacts the performance of AF detection.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!