Special Issue "Recent Advances in ECG Monitoring"
Deadline for manuscript submissions: closed (30 December 2020).
Nowadays, electrocardiogram (ECG) monitoring is drawing great attention because the ECG data—which is noninvasively collected on the human body surface—conveys a multitude of useful information for assessing cardiac disease and patients are able to conduct follow-ups in their own home. ECG monitoring is convenient tool for providing medical assistance to numerous patients at a lower cost. Although ambulatory ECG is widely used to seek anomalies only found in 24-hour-long registers (Holter), the use of automated ECG analysis to assist in medical diagnosis still remains a challenge. Thus, classification of ST morphology for ischemia or fragmented QRS identification as marker of myocardial scar are still open issues; the evaluation of sudden cardiac death risk is also a hot topic, as well as the study of atrial arrhythmias. ECG monitoring has gone beyond one-day observations as interest is currently also focused on monitoring for longer periods of several days. Such monitoring has been found to be useful for the assessment of pathologies and rhythms that are not present in shorter registers. The challenge with these larger data streams is that the treatment of information requires the use of specific methods.
This Special Issue aims to gather articles covering the different stages within any ECG monitoring framework and to offer an overview of the current advances in this area. Contributions related to early stages are welcome, e.g., sensing and collecting data from human body by means of novel sensor technology and electronic devices, or the use of new sampling paradigms such as compressive sensing, including ECG compression. Subsequent preprocessing algorithms to prepare the signal are crucial for the correct functioning at later stages. Important matters to be dealt at this level are noise and artifact reduction, cancelation, and pattern classification; ECG delineation; confident heartbeat extraction; wave identification; and morphology classification. Finally, contributions that support clinical decision making over long-term recordings, like those described in the previous paragraph, are also within the scope of this Special Issue. The recognition of pathology using techniques such as detection, estimation, prediction, and classification by means of probability theory, Bayes inference, or methods relying on machine learning is commonly employed for this purpose.
Prof. Dr. Manuel Blanco-Velasco
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- electrocardiogram (ECG)
- long-term monitoring
- signal compression
- QRS detection
- ECG delineation
- signal quality
- signal processing
- machine learning
- clinical decision making