Book ChapterDOI
Chapter 7 – ECG Signal Processing
Leif Sörnmo
- pp 453-566
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The article was published on 2005-01-01. It has received 63 citations till now.read more
Citations
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Journal ArticleDOI
EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach.
William J. Bosl,William J. Bosl,William J. Bosl,Helen Tager-Flusberg,Charles A. Nelson,Charles A. Nelson +5 more
TL;DR: In this paper, EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months.
Journal ArticleDOI
Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances
TL;DR: The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
Journal ArticleDOI
Analysis of intramuscular electromyogram signals
Roberto Merletti,Dario Farina +1 more
TL;DR: The current clinical use of intramuscular EMG signals relates to the diagnosis of myopathies, of diseases of the α-motor neuron and of the neuromuscular junction through the analysis of the interference signal or of the shape of some motor unit action potentials, usually without a full decomposition of the signal.
Journal ArticleDOI
Biometric authentication based on PCG and ECG signals: present status and future directions
TL;DR: Some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.
Journal ArticleDOI
Electrocardiogram Derived Respiratory Rate from QRS Slopes and R-Wave Angle
Jesus Lazaro,Alejandro Alcaine,Daniel Romero,Eduardo Gil,Pablo Laguna,Esther Pueyo,Raquel Bailon +6 more
TL;DR: A method based on QRS slopes and R-wave angle, which reflect respiration-induced beat morphology variations, outperform those obtained by other reported methods, both in tilt and stress testing.
References
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Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Journal ArticleDOI
Detection of ECG characteristic points using wavelet transforms
TL;DR: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points and the relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated.
Journal ArticleDOI
A wavelet-based ECG delineator: evaluation on standard databases
TL;DR: A robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT), outperforming the results of other well known algorithms, especially in determining the end of T wave.
Journal ArticleDOI
Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database
TL;DR: This work implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process, which has a sensitivity of 99.69 percent and positive predictivity of 98.77 percent when evaluated with the MIT/BIH arrhythmia database.
Journal ArticleDOI
A comparison of the noise sensitivity of nine QRS detection algorithms
TL;DR: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types.
Related Papers (5)
Bioelectrical Signal Processing in Cardiac and Neurological Applications
Leif Sörnmo,Pablo Laguna +1 more