Exploring Transfer Learning for Ventricular Tachycardia Electrophysiology Studies

Arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) are usually identified by looking for abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs). Unfortunately, the accurate recognition of AVPs is a challenging problem for different reasons, including the intrinsic variability in the A VP waveform. Given the high performance of deep neural networks in several scenarios, in this work, we explored the use of transfer learning (TL) for AVPs detection in intracardiac electrophysiology. A balanced set of 1504 bipolar intracardiac EGMs was collected from nine post-ischemic VT patients. The time-frequency representation was generated for each EGM by computing the synchrosqueezed wavelet transform to be used in the re-training of the convolutional neural network. The proposed approach allows obtaining high recognition results, above 90% for all the investigated performance indexes, demonstrating the effectiveness of deep learning in the recognition of AVPs in post-ischemic VT EGMs and paving the way for its use in supporting clinicians in targeting arrhythmogenic sites. In addition, this study further confirms the efficacy of the TL approach even in case of limited dataset sizes

Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning

Background and objective: Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the adoption of explainable deep learning to support the discrimination between physiological EGMs and AVPs. Methods: Three convolutional neural networks were trained to discriminate the target signals based on their time–frequency representations by synchrosqueezed wavelet transform. The efficacy of the method was assessed on 2561 real bipolar EGMs collected from nine post-ischemic VT patients. Results: The proposed approach achieved high performance, with accuracy levels reaching up to 89%. It also demonstrated coherent localization of the arrhythmogenic sites with respect to conventional voltage and local activation time maps. Moreover, by using saliency maps, AVPs discriminant signatures were highlighted at high frequencies (i.e., in the 103–125 Hz band, which was generally relevant for every network), in line with prior evidence. Conclusion: For the first time, deep learning has been successfully applied and robustly evaluated in the field. The proposed approach paves the way to the development of effective AI-driven systems. These systems will enable a faster, trustworthy and operator-independent identification of AVPs in VT EAM procedures. Furthermore, even without injecting prior knowledge in the adopted models, the analysis of saliency maps revealed that CNNs are prone to autonomously select time–frequency ranges of the EGMs in agreement with the current knowledge

A modelling framework to determine vagus nerve functional topography from neural recordings

Background and Objective: Bioelectronic medicine is an emerging field aiming to develop therapies for the treatment of various chronic diseases through targeted organ neuromodulation. One of the most interesting possibilities for administering bioelectronic medicine treatments is through selective stimulation of the autonomic nervous system (ANS), since it plays a crucial role in the control of the whole-body homeostasis. In particular, the vagus nerve stimulation (VNS) is a promising therapy for treatment of various conditions that are resistant to standard medication. However, the fascicular organization of the ANS is surprisingly not yet well understood, with consequent intersubjective variability of the efficacy of the treatments, sometimes with the generation of side effects, due to an unselective stimulation that recruits non-target fibers. Here, an advanced method for the functional characterization of the left vagus nerve was developed, i.e., the identification of the nerve regions involved in cardiovascular or respiratory functions, using a computational model. Methods/Approach: A simulation framework to identify the regions involved in a certain function were created by combining Finite Element Method (FEM) analysis of the macrostructure of the nerve (simulating the distribution of the electrical potential at a given stimulation) and the behaviour of neurons, simulated through the NEURON environment (https://neuron.yale.edu/neuron/). The method used to define the functional topography was to spatially locate the sources associated with a certain function, through the extraction of the discriminative indexes from the recordings. A set of realistic detailed models, designed based on the morphological parameters of the human cervical vagus nerve, were used to simulate realistic neural recordings. Through a model that exploits prior knowledge, i.e., only the geometry of the electrode and the macro structure of the nerve (size and shape), without any information about fascicles distribution, several tests were done to assess the performances of three employed source localization algorithms. In detail, we compare the results of two algorithms present in the literature (Beamforming and Discriminative Field Potential) with a new method presented in this study that we called Discriminative Beamforming (DBF). Main results: The recordings were simulated both with intraneural electrodes (TIME) and with extraneural electrodes (cuff), working with different levels of current sources’ complexity and with different noise level, characterizing the general performance of the algorithms for different electrode types and in different conditions. The DBF results the best performing algorithm and has therefore been adopted to create functional maps for estimating the nerve regions associated with a function. Conclusions/Significance: Achieving an accurate localization in a real case scenario will lead to a functional characterization of the vagus nerve: this in a first place will aims to improve the accuracy of the decoding and subsequently will improve the effectiveness of preclinical and clinical treatments, through more targeted stimulation

Method for determining the functional topography of a peripheral nerve

A method for determining the functional topography of a peripheral nerve of a user comprising the steps of prearranging an electrode comprising a number n of channels ci, with i = 1,2..., n, arranging the electrode in such a way that each channel is in contact with the peripheral nerve at a respective contact point pi, with i = 1,2..., n, generating a model of a cross section S of the peripheral nerve where the area A of the cross section S comprises a number m of areas αj, with j = 1,2,..., m, computing a lead field matrix L = [Rj,i], wherein Rj,i is a value that describes the electrostatic relationship between an area αj and a contact point pi of the cross section S, periodic acquisition, by the electrode, of a number n of voltage values Vki at instants tk, with k = 1,2,...,S, obtaining a voltage matrix V = [Vk,i], with i = 1,2,...,n, where Vki is the voltage value determined by the channel ci at the contact point pi at the instant tk, periodic acquisition, by at least one medical device, of a number r of values of physiological signals Pk,h of the user at instants tk, with k = 1,2,..., s, obtaining a matrix of the physiological signals P = [Pk,h], with h. = 1,2,...,r, where Pk k is the value of the h-th physiological signal determined at the instant tk, computing a discrimination matrix = D = [dh,i], D being function of the matrices V = [Vk,i] and P = [Pk,h], where dh,i is the discrimination coefficient which represents the correlation between the h-th physiological signal Pk,h and the i-th voltage value Vk,i referred to a same instant tk, computing a spatial filtering matrix ΦDBF= [φh,j], φh,j being the localization index which represents the correlation between the h-th physiological signal and the area αj of said cross section S, generating a functional topography of said peripheral nerve, for each h-th physiological signal, wherein each area αj is graphically identified as a function of the corresponding value φh,j associated with it by the spatial filtering matrix ΦDBF

Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning

Background and objective: Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the adoption of explainable deep learning to support the discrimination between physiological EGMs and AVPs. Methods: Three convolutional neural networks were trained to discriminate the target signals based on their time-frequency representations by synchrosqueezed wavelet transform. The efficacy of the method was assessed on 2561 real bipolar EGMs collected from nine post-ischemic VT patients. Results: The proposed approach achieved high performance, with accuracy levels reaching up to 89%. It also demonstrated coherent localization of the arrhythmogenic sites with respect to conventional voltage and local activation time maps. Moreover, by using saliency maps, AVPs discriminant signatures were highlighted at high frequencies (i.e., in the 103-125 Hz band, which was generally relevant for every network), in line with prior evidence. Conclusion: For the first time, deep learning has been successfully applied and robustly evaluated in the field. The proposed approach paves the way to the development of effective AI-driven systems. These systems will enable faster, trustworthy and operator-independent identification of AVPs in VT EAM procedures. Furthermore, even without injecting prior knowledge in the adopted models, the analysis of saliency maps revealed that CNNs are prone to autonomously select time-frequency ranges of the EGMs in agreement with the current knowledge

Guia de prática clínica para o diagnóstico e tipificação da amiloidose: Parte 1/3. Ano 2020

Métodos: Se generó un listado de preguntas con el formato PICO centradas en la especificidad y sensibilidad de las pruebas diagnósticas en amiloidosis. Se realizó la búsqueda en PubMed durante julio-agosto del 2019, en inglés y español. Los niveles de evidencia y los grados de recomendación se basan en el sistema GRADE (http://www.gradeworkinggroup.org/index.htm). Las recomendaciones se graduaron según su dirección (a favor o en contra) y según fuerza (fuertes y débiles). Las recomendaciones finales fueron evaluadas con la herramienta GLIA para barreras y facilitadores en la implementación de éstas. Interpretación de recomendaciones: Las recomendaciones fuertes indican alta confianza, ya sea a favor o en contra, de una intervención. En esta guía se utiliza el lenguaje “se recomienda” cuando se define una recomendación fuerte. Las recomendaciones débiles indican que los resultados para una intervención, favorable o desfavorable, son dudosos. En este caso, se utiliza el lenguaje “se sugiere”, cuando se define una recomendación débil. Como utilizar estas pautas: Las recomendaciones deben ser interpretadas en el contexto de la atención especializada, con estudios diagnósticos validados y realizados por médicos entrenados. Se asume que el médico tratante tiene alto nivel de sospecha de amiloidosis. Asume que los estudios diagnósticos son realizados por médicos entrenados con métodos validados y estandarizados. Esta guía es relevante para los profesionales de la salud y los involucrados en las políticas sanitarias, para ayudar a asegurar que existan los acuerdos necesarios para brindar la atención adecuada. Recomendaciones En pacientes con sospecha de amiloidosis se recomienda: ● La confirmación en el tejido mediante biopsia y tinción con rojo Congo con la característica birrefringencia verde bajo luz polarizada. ● La confirmación mediante microscopía electrónica en el tejido de biopsia. ● La tipificación de la proteína mediante espectrometría de masa. ● La tipificación de la proteína mediante inmunomicroscopía óptica y/o electrónica, en la medida que haya anticuerpos confiables. ● La medición de las cadenas livianas libres séricas para evaluación de un trastorno proliferativo de células plasmáticas monoclonales. ● La Inmunofijación sérica y urinaria para la evaluación de un trastorno proliferativo de células plasmáticas monoclonales. ● La medición de las cadenas livianas libres sérica, más la Inmunofijación sérica y urinaria para la evaluación de un trastorno proliferativo de células plasmáticas monoclonales. En pacientes con sospecha de amiloidosis se sugiere: ● Demostración de un trastorno proliferativo de células plasmáticas monoclonales mediante la demostración de plasmocitos clonales por la técnica más sensible disponible en la médula ósea para el diagnóstico de amiloidosis de tipo AL. ● La confirmación de amiloidosis ATTRv mediante secuenciación de ADN del gen TTR amiloidogénico de los 4 exones en pacientes con sospecha de amiloidosis por ATTRvMethod: Use the PICO format to generate a series of questions, focusing on the specificity and sensitivity of the amyloidosis diagnostic test. PubMed searches were conducted in English and Spanish from July to August 2019. The level of evidence and recommendation are based on the GRADE system (http://www.gradeworkinggroup.org/index.htm). The recommendations are graded according to their direction (for or against) and strength (strong and weak). Finally, it is recommended to use GLIA tools to evaluate the obstacles and facilitators in implementation. Suggested explanation: A strong suggestion indicates a high degree of trust in support or opposition to the intervention. When defining a strong recommendation, this guide uses the "recommended" language. The weaker recommendations indicate that the outcome of the intervention (favorable or unfavorable) is doubtful. In this case, if a weak recommendation is defined, the "recommendation" language is used. How to use these guidelines: Recommendations must be explained within the scope of special care in validated diagnostic studies conducted by specially trained doctors. Presumably, the attending physician has a high degree of suspicion of amyloidosis. It assumes that diagnostic research is conducted by well-trained doctors using a validated standardized method. This guide is intended for health care professionals and those involved in health care policies to help ensure that the necessary agreements have been reached to provide appropriate care. Recommendations For patients with suspected amyloidosis, it is recommended: ● Confirmation in the tissue by biopsy and Congo red staining with the characteristic green birefringence under polarized light is recommended. ● Confirmation by electron microscopy of the biopsy tissue is recommended. ● Protein typing by mass spectrometry is recommended. ● Protein typing by optical and / or electronic immunomicroscopy is recommended, as long as there are reliable antibodies. ● Measurement of serum free light chains is recommended for evaluation of a monoclonal plasma cell proliferative disorder. ● Serum and urinary immunofixation is recommended for evaluation of a monoclonal plasma cell proliferative disorder. ● Measurement of serum free light chains, plus serum and urinary immunofixation is recommended for the evaluation of a monoclonal plasma cell proliferative disorder. For patients suspected of having amyloidosis, it is suggested: ● Demonstration of a monoclonal plasma cell proliferative disorder by demonstration of clonal plasma cells by the most sensitive technique available in the bone marrow for the diagnosis of AL-type amyloidosis. ● Confirmation of ATTRv amyloidosis by DNA sequencing of the 4-exon amyloidogenic TTR gene in patients with suspected ATTRv amyloidosis.Method: Use o formato PICO para gerar uma série de perguntas, com foco no especificidade e sensibilidade do teste diagnóstico de amiloidose. Pesquisas PubMed foram conduzido em inglês e espanhol de julho a agosto de 2019. O nível de evidência e as recomendações são baseadas no sistema GRADE (http://www.gradeworkinggroup.org/index.htm). As recomendações são avaliadas de acordo com sua direção (a favor ou contra) e força (forte e fraca). Enfim, é recomendado o uso de ferramentas GLIA para avaliar os obstáculos e facilitadores em implementação. Explicação sugerida: uma sugestão forte indica um alto nível de confiança no apoio ou oposição à intervenção. Ao definir recomendações fortes, este guia usa uma linguagem "recomendada". As recomendações mais fracas indicam que o resultado da intervenção (favorável ou desfavorável) é duvidoso. Nesse caso, se uma recomendação fraca for definida, a linguagem de "recomendação" será usada. Como usar essas diretrizes: As recomendações devem ser explicadas no contexto de cuidados especializados e estudos de diagnóstico validados realizados por médicos treinados. Suponha que o médico assistente suspeite de um alto nível de amiloidose. Ele presumiu que a pesquisa diagnóstica foi conduzida por médicos bem treinados usando métodos padronizados validados. Este guia se aplica a profissionais de saúde e todos os envolvidos na política de saúde para ajudar a garantir que os arranjos necessários sejam feitos para fornecer cuidados adequados. Em pacientes com suspeita de amiloidose, é recomendado: ● Confirmação do tecido por biópsia e coloração com vermelho do Congo com a birrefringência verde característica sob luz polarizada é recomendada. ● Confirmação por microscopia eletrônica do tecido da biópsia é recomendada. ● Tipagem de proteínas por espectrometria de massa é recomendada. ● Tipagem de proteínas por imunomicroscopia ótica e / ou eletrônica é recomendada, desde que haja anticorpos confiáveis. ● Medição das cadeias leves livres séricas é recomendada para avaliação de um distúrbio proliferativo de células plasmáticas monoclonais. ● Imunofixação sérica e urinária é recomendada para avaliação de um distúrbio proliferativo de células plasmáticas monoclonais. ● Medição das cadeias leves livres séricas, além da imunofixação sérica e urinária, é recomendada para a avaliação de um distúrbio proliferativo de células plasmáticas monoclonais. Em pacientes com suspeita de amiloidose, sugere-se: ● A demonstração de um distúrbio proliferativo de células plasmáticas monoclonais demonstração de plasmócitos clonais pela técnica mais sensível disponível na medula óssea para o diagnóstico de amiloidose do tipo AL. ● A confirmação da amiloidose ATTRv por sequenciamento de DNA do gene TTR amiloidogênico de 4 exon em pacientes com suspeita de amiloidose ATTRv.Fil: Posadas Martinez, Maria Lourdes. Hospital Italiano. Departamento de Medicina. Servicio de Clínica Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Aguirre, Maria Adela. Hospital Italiano. Departamento de Medicina. Servicio de Clínica Médica; ArgentinaFil: Belziti, César. Hospital Italiano; ArgentinaFil: Brouet, Eva. Clinica Giuliani Charata; ArgentinaFil: Auteri, Miguel Angel. Centro Médico de Avanzada; ArgentinaFil: Forte, Ana Luz. Centro Privado; ArgentinaFil: Greloni, Gustavo. Hospital Italiano. Departamento de Medicina. Servicio de Clínica Médica; ArgentinaFil: Marciano, Sebastian. Hospital Italiano. Departamento de Medicina. Servicio de Clínica Médica; ArgentinaFil: Matoso, María Dolores. Hospital Italiano; ArgentinaFil: Perez de Arenaza, Diego. Hospital Italiano; ArgentinaFil: Pitzus, Ariel Edgardo. Hospital Italiano; ArgentinaFil: Rugiero, Marcelo. Hospital Italiano; ArgentinaFil: Saez, María Soledad. Hospital Italiano; ArgentinaFil: Sorroche, Patricia Beatriz. Hospital Italiano; ArgentinaFil: Tomei, Mauricio. Clinica Giuliani Charata; ArgentinaFil: Zinser, Bettina. Clinica Giuliani Charata; ArgentinaFil: Peuchot, Veronica Andrea. Hospital Italiano. Departamento de Medicina. Servicio de Clínica Médica; ArgentinaFil: Nucifora, Elsa Mercedes. Hospital Italiano. Departamento de Medicina. Servicio de Clínica Médica; Argentin