Generation of digital time database from paper ECG records and Fourier transform-based analysis for disease identification

Mitra, S.; Mitra, Mandar; Chaudhuri, B. B.

doi:10.1016/j.compbiomed.2003.08.001

Cited by 30 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Morales et al [9] have managed to produce an improvement by reducing the magnitude of the overall rootmean-square error, but the error still remains significant. In general, the first step in digitization is the separation of the ECG waveform from the grid lines using image intensity histogram [10]. This step is prone to errors as it is completely depended upon the user's ability to observe and select the threshold.…”

Section: Literature Surveymentioning

confidence: 99%

Digitization of Electrocardiogram Using Bilateral Filtering

Sadasivan¹

2020

Preprint

View full text Add to dashboard Cite

Electrocardiogram (ECG) is one of the most basic tools available for the detection of cardiovascular disease (CVD) which has evolved as one of the major causes of death in recent times, spanning countries. Conventionally, ECGs are printed on graph sheets which keep fading away with time. Hence, the preservation of ECG graph sheets is difficult. Digitization of printed ECG graph sheets is a solution to this problem. Digitization also helps in faster analysis and interpretation of ECG signals. Although the previous works have succeeded in smoothing ECG after extracting the signal from graph sheet, the cost amounts to loss of peak amplitude characteristics leading to wrong diagnosis. In this work, we propose a digitization technique to extract ECG signals from graph sheets. The proposed bilateral filter based method achieves a high degree of smoothness with all the peak characteristics intact when compared to other implementations using Butterworth filters. With this technique, it is now possible to create extensive databases similar to MIT-BIH from printed ECG graph sheets. A high level of automation is achieved, and results verified with over 60 ECGs and compared.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Digitization of Electrocardiogram Using Bilateral Filtering

Sadasivan¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The cost function d( f (t i ), 0) can be computed by substituting t j = 0, g(t j ) = 0 and θ (t j ) = 0, φ (t j ) = 0 in Equation 15. The cost function d(0, g(t j )) can be similarly computed.…”

Section: Characterizing Fiducial Point Similaritymentioning

confidence: 99%

“…A number of research and commercial systems exist for digitizing ECG waveforms on hardcopy printouts [18], [5], [13], [12], [14], [15], [16], [4]. They typically start by detecting the ECG waveform and separating it from the background grid, usually via color segmentation or a morphological operator [12].…”

Section: Related Workmentioning

confidence: 99%

Shape-based Matching of ECG Recordings

Syeda-Mahmood

Beymer

Wang

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Abstract-An electrocardiogram (ECG) is an important and commonly used diagnostic aid in cardiovascular disease diagnosis. Physicians routinely perform diagnosis by a simple visual examination of ECG waveform shapes. In this paper, we address the problem of shape-based retrieval of ECG recordings, both digital and scanned from paper, to infer similarity in diagnosed diseases. Specifically, we use the knowledge of ECG recording structure to segment and extract curves representing various recording channels from ECG images. We then present a method of capturing the perceptual shape similarity of ECG waveforms by combining shape matching with dynamic time warping. The shape similarity of each recording channel is combined to develop an overall shape similarity measure between ECG recordings. Results are presented that demonstrate the method on shape-based matching of various cardiovascular diseases.

show abstract

“…Computer system for analysis and extracting features from ECG paper recordings were achieved (Kao et al, 2001). The advances of information technology in storing, retrieving or processing of information are utilized for creation of database after data extraction from ECG paper records (Lobodzinski, 2003;Mitra, 2004). Badilini (2005) presented ECG scan that is a new digital application that enables the user to convert paper ECGs to digital ECGs and validated this study by comparing digital waveforms and measurements with the original.…”

Section: Introductionmentioning

confidence: 99%

Bio-Potential Signal Extraction from Multi-Channel Paper Recorded Charts

AlMejrad¹

2011

American Journal of Applied Sciences

View full text Add to dashboard Cite

Problem statement: Almost all of the modern biomedical equipments that record the biopotential actions have digital output but the paper chart records are a must. The volume of these records is significant and increasing rapidly. Keeping a bio-signal chart of a patient make it easy for quick assessment but still create problems in the essence of data storage, archiving , data interchange and communications. Approach: As a solution to all these problems is to convert these paper records to digital form. In this study, a method for bio-potential signal extraction from single or multi-channel paper recorded charts using image processing techniques is developed. Results: After scanning the paper charts and converting them into images using a commercial scanner, the developed algorithm is applied to eliminate the background of the scanned paper chart from any recording device single channel or multi-channel using binary neighborhood morphological operations then converting the extracted waveform image into quantized values representing the waveform recorded paper chart. The extracted signal is then filtered to remove the high frequency effects that result from the morphological operations. A correlation and frequency analysis procedure is then conducted to verify the result against known sampled waveform. Conclusion: A chart paper conversion to digital values that represent only the values for the biopotential waveform and eliminating the other irrelevant information has been achieved. These resulted in a less space occupation of patient records and make it easy for data further processing and manipulations.

show abstract

Generation of digital time database from paper ECG records and Fourier transform-based analysis for disease identification

Cited by 30 publications

References 14 publications

Digitization of Electrocardiogram Using Bilateral Filtering

Digitization of Electrocardiogram Using Bilateral Filtering

Shape-based Matching of ECG Recordings

Bio-Potential Signal Extraction from Multi-Channel Paper Recorded Charts

Contact Info

Product

Resources

About