Sensor, Signal, and Imaging Informatics in 2017

Hsu, William; Deserno, Thomas M.; Kahn, Charles Η.

doi:10.1055/s-0038-1667084

Cited by 4 publications

(2 citation statements)

References 16 publications

(16 reference statements)

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“…The process of searching the literature for candidate best papers by the SSII section remained a challenging task, given the broad nature of the SSII category. This year, we overhauled the queries that were applied over the last years [ 11 12 13 ]. First, we developed similar queries to search PubMed and Scopus.…”

Section: Paper Selection Processmentioning

confidence: 99%

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2021

Yearb Med Inform

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Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

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Section: Paper Selection Processmentioning

confidence: 99%

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2021

Yearb Med Inform

Self Cite

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“…The advances in machine learning in a wide spectrum of clinical applications have had major impact in fields related to signals such as ECGs [3], [4]. The big-data acquisition from signal datasets make them well suited for machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning

Baydoun¹,

Safatly

Hassan

et al. 2019

IEEE J. Transl. Eng. Health Med.

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Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLABbased tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/− 0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value <0.001, and RR interval 1 +/− 0.001 p-value <0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease. INDEX TERMS Electrocardiogram, digitization, Matlab tool, image processing.

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