12-lead ECG signal processing and atrial fibrillation prediction in clinical practice.

BACKGROUND: Because clinically used 12-lead electrocardiography (ECG) devices have high falsepositive errors in automatic interpretations of atrial fibrillation (AF), they require substantial improvements before use. OBJECTIVE: A clinical 12-lead ECG pre-processing method with a parallel convolutional neural network (CNN) model for 12-lead ECG automatic AF recognition is introduced. METHODS: Raw AF diagnosis data from a 12-lead ECG device were collected and analyzed by two cardiologists to differentiate between true- and false-positives. Using a stationary wavelet transform (SWT) and independent component analysis (ICA) noise reduction was conducted and baseline wandering was corrected for the raw signals. AF patterns were learned and predicted using a parallel CNN deep learning (DL) model. (1) The proposed method alleviates the decreased ECG QRS amplitude enhances the signal-to-noise ratio and clearly shows atrial and ventricular activities. (2) After training, the CNNbased AF detector significantly reduced false-positive errors. The precision of AF diagnosis increased from 77.3% to 94.0 ± 1.5% as compared to ECG device interpretation. For AF screening, the model showed an average sensitivity of 96.8 ± 2.2%, specificity of 79.0 ± 5.8%, precision of 94.0 ± 1.5%, F1-measure of 95.2 ± 1.0%, and overall accuracy of 92.7 ± 1.5%. CONCLUSIONS: The method can bridge the gap between the research and clinical practice The ECG signal pre-processing and DL-based AF interpretation can be rapidly implemented clinically.

A cloud computing based 12-lead ECG telemedicine service.

BACKGROUND: Due to the great variability of 12-lead ECG instruments and medical specialists' interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists' decision making support in emergency telecardiology. METHODS: We create a new cloud and pervasive computing based 12-lead Electrocardiography (ECG) service to realize ubiquitous 12-lead ECG tele-diagnosis. RESULTS: This developed service enables ECG to be transmitted and interpreted via mobile phones. That is, tele-consultation can take place while the patient is on the ambulance, between the onsite clinicians and the off-site senior cardiologists, or among hospitals. Most importantly, this developed service is convenient, efficient, and inexpensive. CONCLUSIONS: This cloud computing based ECG tele-consultation service expands the traditional 12-lead ECG applications onto the collaboration of clinicians at different locations or among hospitals. In short, this service can greatly improve medical service quality and efficiency, especially for patients in rural areas. This service has been evaluated and proved to be useful by cardiologists in Taiwan.

The clinical application of a PACS-dependent 12-lead ECG and image information system in E-medicine and telemedicine.

This study presents a software technology to transform paper-based 12-lead electrocardiography (ECG) examination into (1) 12-lead ECG electronic diagnoses (e-diagnoses) and (2) mobile diagnoses (m-diagnoses) in emergency telemedicine. While Digital Imaging and Communications in Medicine (DICOM)-based images are commonly used in hospitals, the development of computerized 12-lead ECG is impeded by heterogeneous data formats of clinically used 12-lead ECG instrumentations, such as Standard Communications Protocol (SCP) ECG and Extensible Markup Language (XML) ECG. Additionally, there is no data link between clinically used 12-lead ECG instrumentations and mobile devices. To realize computerized 12-lead ECG examination procedures and ECG telemedicine, this study develops a DICOM-based 12-lead ECG information system capable of providing clinicians with medical images and waveform-based ECG diagnoses via Picture Archiving and Communication System (PACS). First, a waveform-based DICOM-ECG converter transforming clinically used SCP-ECG and XML-ECG to DICOM is applied to PACS for image- and waveform-based DICOM file manipulation. Second, a mobile Structured Query Language database communicating with PACS is installed in physicians' mobile phones so that they can retrieve images and waveform-based ECG ubiquitously. Clinical evaluations of this system indicated the following. First, this developed PACS-dependent 12-lead ECG information system improves 12-lead ECG management and interoperability. Second, this system enables the remote physicians to perform ubiquitous 12-lead ECG and image diagnoses, which enhances the efficiency of emergency telemedicine. These findings prove the effectiveness and usefulness of the PACS-dependent 12-lead ECG information system, which can be easily adopted in telemedicine.

Ambulance 12-lead electrocardiography transmission via cell phone technology to cardiologists.

This study demonstrates transmission of 12-lead electrocardiography (ECG) in an ambulance to the cell phone of the attendant emergency medical technician and then to the hospital and to cell phones of off-site cardiologists. The emergency medical technician cell phone receives Extensible Markup Language files generated by a Phillips Extensible Markup Language ECG instrument via Wi-Fi-based wireless network and then sends them to an ECG-processing server at the hospital over the mobile telephone network. After reducing ECG noises and artifacts, the server converts files to Digital Imaging and Communications in Medicine-based ECG reports stored in Picture Archiving and Communication System. These reports are sent to the cell phones of off-site cardiologists. Consequently, on-site Emergency Department physicians and off-site cardiologists can discuss ECG reports via Picture Archiving and Communication System on their computers or cell phones to prepare for the most appropriate treatment while the patient is on the way to the hospital. In conclusion, this 12-lead ECG transmission e-technology expands the functions of a 12-lead ECG instrument and facilitates more efficient prehospital cardiac care.

The realization of ubiquitous 12-lead ECG diagnosis in emergency telemedicine.

This study presents an e-technology to realize a mobile 12-lead electrocardiography (ECG) information system capable of providing clinicians with ubiquitous 12-lead ECG diagnoses. Until now, there has been limited information on mobile systems that can exchange information with clinically used 12-lead ECG for emergency telemedicine. The development of mobilized 12-lead ECG diagnoses in clinical practice is impeded by the heterogeneous 12-lead ECG file formats and compressed ECG waveform data. In this study, clinically used Standard Communications Protocol ECG and Extensible Markup Language ECG interpreters were developed to extract compressed 12-lead ECG waveform data. A 12-lead ECG database server connecting with a hospital information system was then built to collect 12-lead ECG files and related clinical data. Additionally, a mobile 12-lead ECG database was implemented on mobile phones for remote 12-lead ECG diagnoses. Clinical evaluations confirmed the usefulness of this mobile 12-lead ECG information system, which significantly improves the diagnostic accuracy and the performance of medical treatments in cardio-emergency telemedicine. In summary, this system facilitated ubiquitous 12-lead ECG diagnoses and enhanced the efficiency and service quality of emergency telemedicine.

Mobile, cloud, and big data computing: contributions, challenges, and new directions in telecardiology.

Many studies have indicated that computing technology can enable off-site cardiologists to read patients' electrocardiograph (ECG), echocardiography (ECHO), and relevant images via smart phones during pre-hospital, in-hospital, and post-hospital teleconsultation, which not only identifies emergency cases in need of immediate treatment, but also prevents the unnecessary re-hospitalizations. Meanwhile, several studies have combined cloud computing and mobile computing to facilitate better storage, delivery, retrieval, and management of medical files for telecardiology. In the future, the aggregated ECG and images from hospitals worldwide will become big data, which should be used to develop an e-consultation program helping on-site practitioners deliver appropriate treatment. With information technology, real-time tele-consultation and tele-diagnosis of ECG and images can be practiced via an e-platform for clinical, research, and educational purposes. While being devoted to promote the application of information technology onto telecardiology, we need to resolve several issues: (1) data confidentiality in the cloud, (2) data interoperability among hospitals, and (3) network latency and accessibility. If these challenges are overcome, tele-consultation will be ubiquitous, easy to perform, inexpensive, and beneficial. Most importantly, these services will increase global collaboration and advance clinical practice, education, and scientific research in cardiology.