Leif Sörnmo

Bio: Leif Sörnmo is an academic researcher from Lund University. The author has contributed to research in topics: Atrial fibrillation & QRS complex. The author has an hindex of 45, co-authored 324 publications receiving 8334 citations. Previous affiliations of Leif Sörnmo include University of Zaragoza & Siemens.

Bioelectrical Signal Processing in Cardiac and Neurological Applications

Abstract: Chapter 1. Introduction Chapter 2. The Electroencephalogram Chapter 3. EEG Signal Processing Chapter 4. Evoked Potentials Chapter 5. The Electromyogram Chapter 6. The Electrocardiogram Chapter 7. ECG Signal Processing Chapter 8. Heart Rate Variability Appendices Problems

Clustering ECG complexes using Hermite functions and self-organizing maps

Abstract: An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NNs are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation

Abstract: A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.

Principal component analysis in ECG signal processing

Abstract: This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loeve transform is explained. Aspects on PCA related to data with temporal and spatial correlations are considered as adaptive estimation of principal components is. Several ECG applications are reviewed where PCA techniques have been successfully employed, including data compression, ST-T segment analysis for the detection of myocardial ischemia and abnormalities in ventricular repolarization, extraction of atrial fibrillatory waves for detailed characterization of atrial fibrillation, and analysis of body surface potential maps.

Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method

Abstract: Objectives: Atrial fibrillation (AF) in man has previously been shown to include a wide variety of atrial activity. Assessment of the characteristics of this arrhythmia with a commonly applicable tool may therefore be important in the choice and evaluation of different therapeutic strategies. As the AF cycle length has been shown to correlate locally with atrial refractoriness and globally with the degree of atrial organization, with, in general, shorter cycle length during apparently random AF compared to more organized AF, we have developed a new method for non-invasive assessment of the AF cycle length using the surface and the esophagus (ESO) ECG. Methods and Results: From the frequency spectrum of the residual ECG, created by suppression of the QRST complexes, the dominant atrial cycle length (DACL) was derived. By comparison with multiple intracardiac simultaneously acquired right and left AF cycle lengths in patients with paroxysmal AF, we found that the DACL in lead V1, ranging from 130 to 185 ms, well represented a spatial average of the right AF cycle lengths, whereas the DACL in the ESO ECG, ranging from 140 to 185 ms, reflected both the right and the left AF cycle length, where the influence from each structure depended on the atrial anatomy of the individual, as determined by MRI. In patients with chronic AF, the method was capable of following changes in the AF cycle length due to administration of d,l-sotalol and 5 min of ECG recording was sufficient for the DACL to be reproducible. Conclusions: We conclude that this new non-invasive method, named ‘Frequency Analysis of Fibrillatory ECG’ (FAF-ECG), is capable of assessing both the magnitude and the dynamics of the atrial fibrillation cycle length in man.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A Real-Time QRS Detection Algorithm

Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.

Handbook of Blind Source Separation: Independent Component Analysis and Applications

Abstract: Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, RD algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communicationsShows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

A wavelet-based ECG delineator: evaluation on standard databases

Abstract: In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

Automatic classification of heartbeats using ECG morphology and heartbeat interval features

Abstract: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.