RESUMO
Vagus nerve stimulation (VNS) is a therapeutic option in drug-resistant epilepsy. VNS leads to ≥ 50% seizure reduction in 50 to 60% of patients, termed "responders". The remaining 40 to 50% of patients, "non-responders", exhibit seizure reduction < 50%. Our work aims to differentiate between these two patient groups in preimplantation EEG analysis by employing several Entropy methods. We identified 59 drug-resistant epilepsy patients treated with VNS. We established their response to VNS in terms of responders and non-responders. A preimplantation EEG with eyes open/closed, photic stimulation, and hyperventilation was found for each patient. The EEG was segmented into eight time intervals within four standard frequency bands. In all, 32 EEG segments were obtained. Seven Entropy methods were calculated for all segments. Subsequently, VNS responders and non-responders were compared using individual Entropy methods. VNS responders and non-responders differed significantly in all Entropy methods except Approximate Entropy. Spectral Entropy revealed the highest number of EEG segments differentiating between responders and non-responders. The most useful frequency band distinguishing responders and non-responders was the alpha frequency, and the most helpful time interval was hyperventilation and rest 4 (the end of EEG recording).
Assuntos
Epilepsia Resistente a Medicamentos , Estimulação do Nervo Vago , Humanos , Resultado do Tratamento , Estimulação do Nervo Vago/métodos , Entropia , Couro Cabeludo , Hiperventilação , Eletroencefalografia , Convulsões , Epilepsia Resistente a Medicamentos/terapia , Nervo VagoRESUMO
Vagal Nerve Stimulation (VNS) is used to treat patients with pharmacoresistant epilepsy. However, generally accepted tools to predict VNS response do not exist. Here we examined two heart activity measures - mean RR and pNN50 and their complex behavior during activation in pre-implant measurements. The ECG recordings of 73 patients (38 responders, 36 non-responders) were examined in a 30-sec floating window before (120 sec), during (2x120 sec), and after (120 sec) the hyperventilation by nose and mouth. The VNS response differentiation by pNN50 was significant (min p=0.01) in the hyperventilation by a nose with a noticeable descendant trend in nominal values. The mean RR was significant (p=0.01) in the rest after the hyperventilation by mouth but after an approximately 40-sec delay.Clinical Relevance- Our study shows that pNN50 and mean RR can be used to distinguish between VNS responders and non-responders. However, details of dynamic behavior showed how this ability varies in tested measurement segments.
Assuntos
Epilepsia , Estimulação do Nervo Vago , Epilepsia/terapia , Humanos , Próteses e Implantes , DescansoRESUMO
Vagal Nerve Stimulation (VNS) is an option in the treatment of drug-resistant epilepsy. However, approximately a quarter of VNS subjects does not respond to the therapy. In this retrospective study, we introduce heart-rate features to distinguish VNS responders and non-responders. Standard pre-implantation measurements of 66 patients were segmented in relation to specific stimuli (open/close eyes, photic stimulation, hyperventilation, and rests between). Median interbeat intervals were found for each segment and normalized (NMRR). Five NMRRs were significant; the strongest feature achieved significance with p=0.013 and AUC=0.66. Low mutual correlation and independence on EEG signals mean that presented features could be considered as an addition for models predicting VNS response using EEG.
Assuntos
Epilepsia , Estimulação do Nervo Vago , Eletroencefalografia , Epilepsia/terapia , Frequência Cardíaca , Humanos , Estudos RetrospectivosRESUMO
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. The potential value of unbiased, automated EEG classification has long been recognized, and in recent years the application of machine learning methods has received significant attention. A variety of solutions using convolutional neural networks (CNN) for EEG classification have emerged with impressive results. However, interpretation of CNN results and their connection with underlying basic electrophysiology has been unclear. This paper proposes a CNN architecture, which enables interpretation of intracranial EEG (iEEG) transients driving classification of brain activity as normal, pathological or artifactual. The goal is accomplished using CNN with long short-term memory (LSTM). We show that the method allows the visualization of iEEG graphoelements with the highest contribution to the final classification result using a classification heatmap and thus enables review of the raw iEEG data and interpret the decision of the model by electrophysiology means.
Assuntos
Aprendizado Profundo , Eletroencefalografia/classificação , Artefatos , Conjuntos de Dados como Assunto , Eletroencefalografia/instrumentação , Eletroencefalografia/métodos , Humanos , Curva ROCRESUMO
OBJECTIVE: This paper describes a method for automated discrimination of heart sounds recordings according to the Physionet Challenge 2016. The goal was to decide if the recording refers to normal or abnormal heart sounds or if it is not possible to decide (i.e. 'unsure' recordings). APPROACH: Heart sounds S1 and S2 are detected using amplitude envelopes in the band 15-90 Hz. The averaged shape of the S1/S2 pair is computed from amplitude envelopes in five different bands (15-90 Hz; 55-150 Hz; 100-250 Hz; 200-450 Hz; 400-800 Hz). A total of 53 features are extracted from the data. The largest group of features is extracted from the statistical properties of the averaged shapes; other features are extracted from the symmetry of averaged shapes, and the last group of features is independent of S1 and S2 detection. Generated features are processed using logical rules and probability assessment, a prototype of a new machine-learning method. MAIN RESULTS: The method was trained using 3155 records and tested on 1277 hidden records. It resulted in a training score of 0.903 (sensitivity 0.869, specificity 0.937) and a testing score of 0.841 (sensitivity 0.770, specificity 0.913). The revised method led to a test score of 0.853 in the follow-up phase of the challenge. SIGNIFICANCE: The presented solution achieved 7th place out of 48 competing entries in the Physionet Challenge 2016 (official phase). In addition, the PROBAfind software for probability assessment was introduced.
Assuntos
Inteligência Artificial , Ruídos Cardíacos , Processamento de Sinais Assistido por Computador , Algoritmos , Probabilidade , SoftwareRESUMO
False alarms in intensive care units represent a serious threat to patients. We propose a method for detection of five live-threatening arrhythmias. It is designed to work with multimodal data containing electrocardiograph and arterial blood pressure or photoplethysmograph signals. The presented method is based on descriptive statistics and Fourier and Hilbert transforms. It was trained using 750 records. The method was validated during the follow-up phase of the CinC/Physionet Challenge 2015 on a hidden dataset with 500 records, achieving a sensitivity of 93% (95%) and a specificity of 87% (88%) for real-time (retrospective) files. The given sensitivity and specificity resulted in score of 81.62 (84.96) for real-time (retrospective) records. The presented method is an improved version of the original algorithm awarded the first and the second prize in CinC/Physionet Challenge 2015.
Assuntos
Algoritmos , Arritmias Cardíacas/diagnóstico , Alarmes Clínicos , Unidades de Terapia Intensiva , Monitorização Fisiológica/instrumentação , Processamento de Sinais Assistido por Computador , Arritmias Cardíacas/fisiopatologia , Pressão Sanguínea , Eletrocardiografia/instrumentação , Reações Falso-Positivas , Humanos , Aprendizado de Máquina , Fotopletismografia/instrumentaçãoRESUMO
The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant-a stand-alone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75 × 10(6) samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by third parties ensuring its adaptability to future research tasks and new data formats.
Assuntos
Processamento de Sinais Assistido por Computador , Software , Acesso à Informação , Eletrocardiografia/métodos , Eletroencefalografia/métodos , Internet , Fatores de TempoRESUMO
This study introduces a method for detection of ventricular depolarization activity and the transfer of this activity into an audible stereo audio signal. Heart potentials are measured by an ultra-high-frequency high-dynamic-range electrocardiograph (UHF-ECG) with a 25-kHz sampling rate. Averaged and prolonged UHF amplitude envelopes of V1-3 and V4-6 leads at a frequency range of 500-1000 Hz are used as a modulating function for two carrier audio frequencies. The right speaker makes it possible to listen to the depolarization of the septum and right ventricle (V1-3) and the left speaker the left ventricle lateral wall (V4-6). In the healthy heart, both speakers can be heard simultaneously. A delayed L or R speaker represents the dyssynchronous electrical activation of the ventricles. Examples of the normal heart, right bundle branch block and left bundle branch block can be heard at www.medisig.com/uhfecg.
Assuntos
Eletrocardiografia/métodos , Ventrículos do Coração/fisiopatologia , Processamento de Sinais Assistido por Computador , Função Ventricular/fisiologia , Bloqueio de Ramo/fisiopatologia , Sistema de Condução Cardíaco , HumanosRESUMO
We present an off-line analysis procedure for exploring brain activity recorded from intra-cerebral electroencephalographic data (SEEG). The objective is to determine the statistical differences between different types of stimulations in the time-frequency domain. The procedure is based on computing relative signal power change and subsequent statistical analysis. An example of characteristic statistically significant event-related de/synchronization (ERD/ERS) detected across different frequency bands following different oddball stimuli is presented. The method is used for off-line functional classification of different brain areas.