Role of activation signatures in re-entrant ventricular tachycardia circuits.

INTRODUCTION: Development of a rapid means to verify the ventricular tachycardia (VT) isthmus location from heart surface electrogram recordings would be a helpful tool for the electrophysiologist. METHOD: Myocardial infarction was induced in 22 canines by left anterior descending coronary artery ligation under general anesthesia. After 3-5 days, VT was inducible via programmed electrical stimulation at the anterior left ventricular epicardial surface. Bipolar VT electrograms were acquired from 196 to 312 recording sites using a multielectrode array. Electrograms were marked for activation time, and activation maps were constructed. The activation signal, or signature, is defined as the cumulative number of recording sites that have activated per millisecond, and it was utilized to segment each circuit into inner and outer circuit pathways, and as an estimate of best ablation lesion location to prevent VT. RESULTS: VT circuit components were differentiable by activation signals as: inner pathway (mean: 0.30 sites activating/ms) and outer pathway (mean: 2.68 sites activating/ms). These variables were linearly related (p < .001). Activation signal characteristics were dependent in part upon the isthmus exit site. The inner circuit pathway determined by the activation signal overlapped and often extended beyond the activation map isthmus location for each circuit. The best lesion location estimated by the activation signal would likely block an electrical impulse traveling through the isthmus, to prevent VT in all circuits. CONCLUSIONS: The activation signal algorithm, simple to implement for real-time computer display, approximates the VT isthmus location and shape as determined from activation marking, and best ablation lesion location to prevent reinduction.

Automated prediction of isthmus areas in scar-related atrial tachycardias using artificial intelligence.

INTRODUCTION: Ablation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses. METHODS: Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas. RESULTS: A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group. CONCLUSION: A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.

Psychological, Psychiatric, and Organic Brain Manifestations of Celiac Disease.

Celiac disease is an autoimmune condition that affects approximately 1% of the population worldwide. Although its main impact often concerns the small intestine, resulting in villous atrophy and nutrient malabsorption, it can also cause systemic manifestations, particularly when undiagnosed or left untreated. Here, attention is paid to the possible psychological, psychiatric, and organic brain manifestations of celiac disease. Specific topics related to the influence and risk of such manifestations with respect to celiac disease are defined and discussed. Overall, eighteen main topics are considered, sifted from over 500 references. The most often studied topics were found to be the effect on quality of life, organic brain dysfunction and ataxia, epilepsy, Down syndrome, generalized psychological disorders, eating dysfunction, depression, and schizophrenia. For most every topic, although many studies report a connection to celiac disease, there are often one or more contrary studies and opinions. A bibliographic analysis of the cited articles was also done. There has been a sharp increase in interest in this research since 1990. Recently published articles tend to receive more referencing, up to as many as 15 citations per year, suggesting an increasing impact of the topics. The number of manuscript pages per article has also tended to increase, up to as many as 12 pages. The impact factor of the publishing journal has remained level over the years. This compendium may be useful in developing a consensus regarding psychological, psychiatric, and organic brain manifestations that can occur in celiac disease, and for determining the best direction for ongoing research focus.

Celiac Disease and the Risk of Cardiovascular Diseases.

Celiac disease (CD) is a chronic autoimmune disorder that affects the small intestine in genetically predisposed individuals. Previous studies have investigated the potential link between CD and cardiovascular disease (CVD); however, the findings have been inconsistent. We aimed to provide an updated review of the literature on the association between CD and CVD. PubMed was searched from inception to January 2023 using keywords including CD, cardiovascular disease, coronary artery disease, cardiac arrhythmia, heart failure, cardiomyopathy, and myocarditis. We summarized the results of the studies, including meta-analyses and original investigations, and presented them according to the different forms of CVD. Meta-analyses published in 2015 provided mixed results regarding the relationship between CD and CVD. However, subsequent original investigations have shed new light on this association. Recent studies indicate that individuals with CD are at a higher risk of developing overall CVD, including an increased risk of myocardial infarction and atrial fibrillation. However, the link between CD and stroke is less established. Further research is needed to determine the link between CD and other cardiac arrhythmias, such as ventricular arrhythmia. Moreover, the relationship between CD and cardiomyopathy or heart failure, as well as myopericarditis, remains ambiguous. CD patients have a lower prevalence of traditional cardiac risk factors, such as smoking, hypertension, hyperlipidemia, and obesity. Therefore, it is important to discover strategies to identify patients at risk and reduce the risk of CVD in CD populations. Lastly, it is unclear whether adherence to a gluten-free diet can diminish or increase the risk of CVD among individuals with CD, necessitating further research in this area. To fully comprehend the correlation between CD and CVD and to determine the optimal prevention strategies for CVD in individuals with CD, additional research is necessary.

Impact of Celiac Disease on Dating.

BACKGROUND/AIMS: When seeking a romantic partner, individuals with celiac disease (CD) must navigate challenging social situations. We aimed to investigate dating-related behaviors in adults with CD. METHODS: A total of 11,884 affiliates of the Celiac Disease Center at Columbia University were invited to participate in an online survey. Adults (≥ 18 years) with biopsy-diagnosed CD were included. Among the 5,249 who opened the email, 538 fully completed the survey (10.2%). The survey included a CD-specific dating attitudes/behaviors questionnaire, a Social Anxiety Questionnaire (SAQ), a CD-specific quality of life instrument (CD-QOL), and a CD Food Attitudes and Behaviors scale (CD-FAB). RESULTS: Respondents were primarily female (86.8%) and the plurality (24.4%) was in the 23-35 year age range. 44.3% had dated with CD, and among them, 68.4% reported that CD had a major/moderate impact on their dating life. A major/moderate impact was more commonly reported among females (69.3%, p < 0.001), 23-35-year-olds (77.7%, p = 0.015), those with a household income < $50 K (81.7%, p = 0.019), and those with a lower CD-QOL score (50.5 vs. 73.4, p = 0.002). While on dates, 39.3% were uncomfortable explaining precautions to waiters, 28.2% engaged in riskier eating behaviors, and 7.5% intentionally consumed gluten. 39.0% of all participants were hesitant to kiss their partner because of CD; females more so than males (41.1% vs. 22.7%, p = 0.005). CONCLUSIONS: The majority of participants felt that CD had a major/moderate impact on their dating life. This impact may result in hesitation toward dating and kissing, decreased QOL, greater social anxiety, and less adaptive eating attitudes and behaviors. CD and the need to adhere to a gluten free diet have a major impact on dating and intimacy.

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

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.

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images.

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images.

Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.

Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques.

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.

Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors.

Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.

Atrial Tachycardias After Atrial Fibrillation Ablation Manifest Different Waveform Characteristics: Implications for Characterizing Tachycardias.

INTRODUCTION: Atrial fibrillation (AF) ablation patients often manifest atrial tachycardias (AT) with atypical ECG morphologies that preclude accurate localization and mechanism. Diagnostic maneuvers used to define ATs during electrophysiology studies can be limited by tachycardia termination or transformation. Additional methods of characterizing post-AF ablation ATs are required. METHODS AND RESULTS: We evaluated the utility of noninvasive ECG signal analytics in postablation AF patients for the following features: (1) Localization of ATs (i.e., right vs. left atrium), and (2) Identification of common left AT mechanisms (i.e., focal vs. macroreentrant). Atrial waveforms from the surface ECG were used to analyze (1) spectral organization, including dominant amplitude (DA) and mean spectral profile (MP), and (2) temporospatial variability, using temporospatial correlation coefficients. We studied 94 ATs in 71 patients who had undergone prior pulmonary vein isolation for AF and returned for a second ablation: (1) right atrial cavotricuspid-isthmus dependent (CTI) ATs (n = 21); (2) left atrial macroreentrant ATs (n = 41) and focal ATs (n = 32). Right CTI ATs manifested higher DAs and lower MPs than left ATs, indicative of greater stability and less complexity in the frequency spectrum. Left macroreentrant ATs possessed higher temporospatial organization than left focal ATs. CONCLUSIONS: Noninvasively recorded atrial waveform signal analyses show that right ATs possess more stable activation properties than left ATs, and left macroreentrant ATs manifest higher temporospatial organization than left focal ATs. Further prospective analyses evaluating the role these novel ECG-derived tools can play to help localize and identify mechanisms of common ATs in AF ablation patients are warranted.

Mechanism-specific effects of adenosine on ventricular tachycardia.

INTRODUCTION: There is no universally accepted method by which to diagnose clinical ventricular tachycardia (VT) due to cAMP-mediated triggered activity. Based on cellular and clinical data, adenosine termination of VT is thought to be consistent with a diagnosis of triggered activity. However, a major gap in evidence mitigates the validity of this proposal, namely, defining the specificity of adenosine response in well-delineated reentrant VT circuits. To this end, we systematically studied the effects of adenosine in a model of canine reentrant VT and in human reentrant VT, confirmed by 3-dimensional, pace- and substrate mapping. METHODS AND RESULTS: Adenosine (12 mg [IQR 12-24]) failed to terminate VT in 31 of 31 patients with reentrant VT due to structural heart disease, and had no effect on VT cycle length (age, 67 years [IQR 53-74]); ejection fraction, 35% [IQR 20-55]). In contrast, adenosine terminated VT in 45 of 50 (90%) patients with sustained focal right or left outflow tract tachycardia. The sensitivity of adenosine for identifying VT due to triggered activity was 90% (95% CI, 0.78-0.97) and its specificity was 100% (95% CI, 0.89-1.0). Additionally, reentrant circuits were mapped in the epicardial border zone of 4-day-old infarcts in mongrel dogs. Adenosine (300-400 µg/kg) did not terminate sustained VT or have any effect on VT cycle length. CONCLUSION: These data support the concept that adenosine's effects on ventricular myocardium are mechanism specific, such that termination of VT in response to adenosine is diagnostic of cAMP-mediated triggered activity.

Ventricular cycle length characteristics estimative of prolonged RR interval during atrial fibrillation.

BACKGROUND: When atrial fibrillation (AF) is incessant, imaging during a prolonged ventricular RR interval may improve image quality. It was hypothesized that long RR intervals could be predicted from preceding RR values. METHODS: From the PhysioNet database, electrocardiogram RR intervals were obtained from 74 persistent AF patients. An RR interval lengthened by at least 250 ms beyond the immediately preceding RR interval (termed T0 and T1, respectively) was considered prolonged. A two-parameter scatterplot was used to predict the occurrence of a prolonged interval T0. The scatterplot parameters were: (1) RR variability (RRv) estimated as the average second derivative from 10 previous pairs of RR differences, T13-T2, and (2) Tm-T1, the difference between Tm, the mean from T13 to T2, and T1. For each patient, scatterplots were constructed using preliminary data from the first hour. The ranges of parameters 1 and 2 were adjusted to maximize the proportion of prolonged RR intervals within range. These constraints were used for prediction of prolonged RR in test data collected during the second hour. RESULTS: The mean prolonged event was 1.0 seconds in duration. Actual prolonged events were identified with a mean positive predictive value (PPV) of 80% in the test set. PPV was >80% in 36 of 74 patients. An average of 10.8 prolonged RR intervals per 60 minutes was correctly identified. CONCLUSIONS: A method was developed to predict prolonged RR intervals using two parameters and prior statistical sampling for each patient. This or similar methodology may help improve cardiac imaging in many longstanding persistent AF patients.

Temporal stability in the spectral representation of complex fractionated atrial electrograms.

BACKGROUND: Although local electrograms during atrial fibrillation (AF) are often spectrally analyzed over 8-second (8s) intervals, changes may be common over intervals as short as 2s. We sought to determine whether averaged 2s measurements of electrogram spectral parameters were similar to 8s measurements, and whether the 2s intervals could provide an estimate of the temporal stability of the signal frequency content in paroxysmal versus persistent AF. METHODS: Complex fractionated atrial electrograms (CFAEs) were acquired outside the pulmonary vein ostia and from free wall sites in nine paroxysmal and 10 longstanding persistent AF patients. Using a 2s sliding calculation window, a frequency spectrum was computed every 100 ms over an interval of 8.4 seconds (82 spectra in total). The dominant frequency (DF), the dominant amplitude (DA), and the mean spectral profile (MP) were measured. The 2s measurements were compared to single 8.4-second interval measurements. Coefficients of variation (COV) were computed from the 82 spectra for each CFAE recording to determine temporal variability of parameters. RESULTS: Over the sliding 2s computation intervals, as for fixed 8.4-second computation intervals, mean DA and DF were significantly higher in longstanding persistent AF while MP was significantly higher in paroxysmal AF (P ≤ 0.001). The COV was significantly higher for the DF parameter in paroxysmal AF (P < 0.001) and significantly higher for the MP parameter in persistent AF (P < 0.02). CONCLUSIONS: For both paroxysmal and persistent AF data, the 2s sliding window averages provide similar results to single 8.4-second intervals, and information regarding temporal stability was additionally obtained in the process.

Software algorithm and hardware design for real-time implementation of new spectral estimator.

BACKGROUND: Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). METHOD: Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. RESULTS: The average interval for a single real-time spectral calculation in software was 3.29 µs for NSE versus 504.5 µs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. CONCLUSIONS: The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time.

Sinus rhythm activation signature indicates reentrant ventricular tachycardia inducibility and approximate isthmus location.

BACKGROUND: Sinus rhythm activation time is useful to assess infarct border zone substrate. OBJECTIVE: We sought to further investigate sinus activation in ventricular tachycardia (VT). METHODS: Canine postinfarction data were analyzed retrospectively. In each experiment, an infarct was created in the left ventricular wall by left anterior descending coronary artery ligation. At 3 to 5 days after ligation, 196-312 bipolar electrograms were recorded from the anterior left ventricular epicardium overlapping the infarct border zone. Sustained monomorphic VT was induced by premature electrical stimulation in 50 experiments and was noninducible in 43 experiments. Acquired sinus rhythm and VT electrograms were marked for electrical activation time, and activation maps of representative sinus rhythm and VT cycles were constructed. The sinus rhythm activation signature was defined as the cumulative number of multielectrode recording sites that had activated per time epoch, and its derivative was used to predict VT inducibility and to define the sinus rhythm slow/late activation sequence. RESULTS: Plotting mean activation signature derivative, a best cutoff value was useful to separate experiments with reentrant VT inducibility (sensitivity, 42/50) vs noninducibility (specificity, 39/43), with an accuracy of 81 of 93. For the 50 experiments with inducible VT, recording sites overlying a segment of isochrone encompassing the sinus rhythm slow/late activation sequence spanned the VT isthmus location in 32 cases (64%), partially spanned it in 15 cases (30%), but did not span it in 3 cases (6%). CONCLUSION: The sinus rhythm activation signature derivative is assistive to differentiate substrate supporting reentrant VT inducibility vs noninducibility and to identify slow/late activation for targeting isthmus location.

Wavefront curvature analysis derived from preprocedural imaging can identify the critical isthmus in patients with postinfarcted ventricular tachycardia.

BACKGROUND: Where activation wavefront curvature is convexly shaped, functional conduction block can occur. OBJECTIVE: The purpose of this study was to determine whether left ventricular (LV) wall thickness determined from contrast-enhanced computed tomography (CT) is useful in localizing such areas in clinical postinfarction reentrant ventricular tachycardia (VT). METHODS: We evaluated data from 6 patients who underwent catheter ablation for postinfarction VT. CT imaging with inHEART processing was conducted 1-3 days before electrophysiological (EP) study to determine LV wall thickness (T). Activation wavefront curvature was approximated as ΔT/T, where ΔT represents wall thickness change. During EP study, bipolar LV VT electrograms were acquired using a high-density mapping catheter, and activation times were determined. Maps of T, ΔT/T, and VT activation were subsequently compared using statistical analyses. RESULTS: Two of 6 cases exhibited dual circuit morphologies, resulting in a total of 8 VT morphologies analyzed. The LV wall near the VT isthmus location tended to be thin, on the order of a few hundred micrometers. Regions of largest ΔT/T partially coincided with the lateral isthmus boundaries where electrical conduction block occurred during VT. ΔT/T at the boundaries, measured from imaging, was significantly larger compared to values at the isthmus midline and to the global LV mean value (P <.001). CONCLUSION: Wavefront curvature measured by ΔT/T and caused by source-sink mismatch is dependent on ventricular wall thickness. Areas of high wavefront curvature partly coincide with and may be helpful in locating the VT isthmus in infarct border zones using preprocedural imaging analysis.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.

BACKGROUND: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND METHODS: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. RESULTS: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. CONCLUSIONS: Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.