Book review of applied medical image processing: A basic course

This article is a review of the book: 'Applied Medical Image Processing: A Basic Course', by Wolfgang Birkfellner, which is published by CRC Press. Basic information that should be helpful in deciding whether to read the book and whether to use it as a course textbook is presented. This includes an introduction, the suitability of the book for use in coursework, its coverage of medical imaging and image processing, discussion and conclusions, and an appendix with a relevant computer program for extracting medical images

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Review of biomedical signal and image processing

This article is a review of the book “Biomedical Signal and Image Processing” by Kayvan Najarian and Robert Splinter, which is published by CRC Press, Taylor & Francis Group. It will evaluate the contents of the book and discuss its suitability as a textbook, while mentioning highlights of the book, and providing comparison with other textbooks

Review of Biomedical Image Processing

This article is a review of the book: 'Biomedical Image Processing', by Thomas M. Deserno, which is published by Springer-Verlag. Salient information that will be useful to decide whether the book is relevant to topics of interest to the reader, and whether it might be suitable as a course textbook, are presented in the review. This includes information about the book details, a summary, the suitability of the text in course and research work, the framework of the book, its specific content, and conclusions

Ibutilide Increases the Variability and Complexity of Atrial Fibrillation Electrograms: Antiarrhythmic Insights Using Signal Analyses

Introduction Intravenous ibutilide is used to convert atrial fibrillation (AF) to sinus rhythm (SR) due to its Class III antiarrhythmic mechanisms. However, the effects of ibutilide on local electrograms (EGMs) during AF have not been elucidated. Methods and Results We used EGM analysis techniques to characterize how ibutilide administration changes the frequency, morphology, and repeatability of AF EGM signals, thereby providing insight into ibutilide's antiarrhythmic mechanism of action. AF recordings were collected from 21 patients with AF, both before and after ibutilide administration. The effects of ibutilide on the following AF EGM parameters were assessed: (1) dominant frequency (DF), (2) variations in EGM amplitude and overall morphology, (3) repetition of EGM patterns, and (4) complexity of the AF frequency spectra. When comparing pre- versus post-ibutilide administration EGMs, DF decreased from 5.45 Hz to 4.02 Hz (P < 0.0001). There was an increase in the variability of both AF EGM amplitudes (P = 0.003) and overall AF EGM morphologies (P = 0.003). AF EGM pattern repetitiveness decreased (P = 0.01), and the AF frequency spectral profile manifested greater complexity (P = 0.02). Conclusions Novel EGM signal analysis techniques reveal that ibutilide administration causes increased complexity in the atrial electrical activation pattern with decreasing rate. These findings may be explained by the progressive destabilization of higher frequency, more homogeneous primary drivers of AF over the course of ibutilide administration, and/or less uniform propagation of atrial activation, until AF maintenance becomes more difficult and either transforms to atrial tachycardia or terminates to SR

Measurement and monitoring of electrocardiogram belt tension in premature infants for assessment of respiratory function

Background: Monitoring of the electrocardiogram (ECG) in premature infants with conventional adhesive-backed electrodes can harm their sensitive skin. Use of an electrode belt prevents skin irritation, but the effect of belt pressure on respiratory function is unknown. A strain gauge sensor is described which measures applied belt tension. Method: The device frame was comprised of an aluminum housing and slide to minimize the device weight. Velcro tabs connected housing and slide to opposite tabs located at the electrode belt ends. The slide was connected to a leaf spring, to which were bonded two piezoresistive transducers in a half-bridge circuit configuration. The device was tested for linearity and calibrated. The effect on infant respiratory function of constant belt tension in the normal range (30 g–90 g) was determined. Results: The mechanical response to a step input was second order (f_n = 401 Hz, ζ = 0.08). The relationship between applied tension and output voltage was linear in the range 25–225 gm of applied tension (r² = 0.99). Measured device sensitivity was 2.18 mV/gm tension using a 5 V bridge excitation voltage. When belt tension was increased in the normal range from 30 gm to 90 gm, there was no significant change in heart rate and most respiratory functions during monitoring. At an intermediate level of tension of 50 gm, pulmonary resistance and work of breathing significantly decreased. Conclusion: The mechanical and electrical design of a device for monitoring electrocardiogram electrode belt tension is described. Within the typical range of application tension, cardiovascular and respiratory function are not substantially negatively affected by electrode belt force

Study on the Stability of CFAEs to Characterize the Atrial Substrate in Atrial Fibrillation

[EN] A variety of indexes has been applied to complex fractionated atrial electrograms (CFAEs) of atrial fibrillation (AF) aimed at characterizing the atrial substrate. However, often the reported results miss the assessment of intra-recording and intra-patient stability of the analyzed data, as well as CFAEs signal quality. This work introduces a study in which Determinism (DET) of Recurrence Quantification Analysis (RQA) and Sample Entropy (SE) have been applied to assess intra-recording and intrapatient stability of 1, 2 and 4 s-length segments CFAEs recorded from patients with paroxysmal and persistent AF using the coefficient of variation (CV). Furthermore, the analyses verified changes introduced by discarding artifacted and noisy CFAE segments. The intra-recording analysis pointed out that discarding segments provoked a significant variation of CV(%) in any segment length both for DET and SE, with deeper decreases for longer segments. Intra-patient stability provided large variations in CV(%) for DET and even bigger for SE at any segment length. In this case discarding segments was useless and CV provided limited variations. Kruskal-Wallis test revealed significant differences in DET and SE values among channels, independently from the discarding process.Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.Finotti, E.; Ciaccio, EJ.; Garan, H.; Hornero, F.; Alcaraz, R.; Rieta, JJ. (2020). Study on the Stability of CFAEs to Characterize the Atrial Substrate in Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.252S1

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers

Discrimination Between CFAEs of Paroxysmal and Persistent Atrial Fibrillation With Simple Classification Models of Reduced Features

[EN] A significant number of variables to discriminate between paroxysmal and persistent atrial fibrillation (ParAF vs. PerAF) has been widely exploited, mostly assessed with statistical tests aimed to suggest adequate approaches for catheter ablation (CA) of AF. However, in practice, it would be desirable to utilize simple classification models readily understandable. In this work dominant frequency (DF), AF cycle length (AFCL), sample entropy (SE) and determinism (DET) of recurrent quantification analysis were applied to recordings of complex fractionated atrial electrograms (CFAEs) of AF patients, aimed to create simple models to discriminate between ParAF and PerAF. Correlation matrix filters removed redundant information and Random Forests ranked the variables by relevance. Next, coarse tree models were built, optimally combining high-ranking indexes, and tested with leave-one-out cross-validation. The best classification performance combined SE and DF with an Accuracy (Acc) of 88.2% to discriminate ParAF from PerAF, while the highest single Acc was provided by DET reaching 82.4%. Hence, careful selection of reduced sets of features feeding simple classification models is able to discriminate accurately between CFAEs of ParAF and PerAFFinotti, E.; Ciaccio, EJ.; Garan, H.; Bertomeu-Gonzalez, V.; Alcaraz, R.; Rieta, JJ. (2020). Discrimination Between CFAEs of Paroxysmal and Persistent Atrial Fibrillation With Simple Classification Models of Reduced Features. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.360S1

Practical Considerations for the Application of Nonlinear Indices Characterizing the Atrial Substrate in Atrial Fibrillation

[EN] Atrial fibrillation (AF) is the most common cardiac arrhythmia, and in response to increasing clinical demand, a variety of signals and indices have been utilized for its analysis, which include complex fractionated atrial electrograms (CFAEs). New methodologies have been developed to characterize the atrial substrate, along with straightforward classification models to discriminate between paroxysmal and persistent AF (ParAF vs. PerAF). Yet, most previous works have missed the mark for the assessment of CFAE signal quality, as well as for studying their stability over time and between different recording locations. As a consequence, an atrial substrate assessment may be unreliable or inaccurate. The objectives of this work are, on the one hand, to make use of a reduced set of nonlinear indices that have been applied to CFAEs recorded from ParAF and PerAF patients to assess intra-recording and intra-patient stability and, on the other hand, to generate a simple classification model to discriminate between them. The dominant frequency (DF), AF cycle length, sample entropy (SE), and determinism (DET) of the Recurrence Quantification Analysis are the analyzed indices, along with the coefficient of variation (CV) which is utilized to indicate the corresponding alterations. The analysis of the intra-recording stability revealed that discarding noisy or artifacted CFAE segments provoked a significant variation in the CV(%) in any segment length for the DET and SE, with deeper decreases for longer segments. The intra-patient stability provided large variations in the CV(%) for the DET and even larger for the SE at any segment length. To discern ParAF versus PerAF, correlation matrix filters and Random Forests were employed, respectively, to remove redundant information and to rank the variables by relevance, while coarse tree models were built, optimally combining high-ranked indices, and tested with leave-one-out cross-validation. The best classification performance combined the SE and DF, with an accuracy (Acc) of 88.3%, to discriminate ParAF versus PerAF, while the highest single Acc was provided by the DET, reaching 82.2%. This work has demonstrated that due to the high variability of CFAEs data averaging from one recording place or among different recording places, as is traditionally made, it may lead to an unfair oversimplification of the CFAE-based atrial substrate characterization. Furthermore, a careful selection of reduced sets of features input to simple classification models is helpful to accurately discern the CFAEs of ParAF versus PerAF.This research has received partial financial support from public national grants DPI2017-83952-C3, PID2021-00X128525-IV0, and PID2021-123804OB-I00 of the Spanish Government with DOI 10.13039/501100011033 jointly with the European Regional Development Fund (EU), and regional grants SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2021/286 from Generalitat Valenciana.Finotti, E.; Quesada, A.; Ciaccio, EJ.; Garan, H.; Hornero, F.; Alcaraz, R.; Rieta, JJ. (2022). Practical Considerations for the Application of Nonlinear Indices Characterizing the Atrial Substrate in Atrial Fibrillation. Entropy. 24(24):1-17. https://doi.org/10.3390/e24091261117242