Sardar Ansari

Role of adenosine after antral pulmonary vein isolation of paroxysmal atrial fibrillation: A randomized controlled trial

BACKGROUND Adenosine can reveal dormant pulmonary vein (PV) conduction after PV isolation (PVI) in patients with paroxysmal atrial fibrillation (AF). However, the impact of elimination of adenosine-provoked dormant PV conduction after PVI has not been formally evaluated. OBJECTIVE The purpose of this study was to determine whether ablation of PV reconnections unmasked by adenosine improves outcomes. METHODS Patients with paroxysmal AF (n = 129) were randomized to receive either adenosine (n = 61) or no adenosine (n = 68) after PVI. Dormant conduction revealed by adenosine after PVI was ablated until all adenosine-mediated reconnections were eliminated. Thereafter, both groups received isoproterenol. RESULTS Acute reconnection was seen in 23 patients (37%) in the adenosine group. There was a significant difference between the number of PVs reconnected if patients were given adenosine >60 minutes after initial PVI compared to those who received adenosine <60 minutes after initial PVI (3/32 [9.4%] vs 24/32 [75%], P <.0001). Patients who did not receive adenosine had more PV reconnections after isoproterenol infusion compared to patients in the adenosine group (17/68 [25.0%] vs 5/61 [8.2%], P = .018). There was no difference in the rate of AF recurrence in patients who received adenosine (24/61 [39%]) compared to control patients (23/68 [34%], log-rank P = .83). CONCLUSION Adenosine can reveal dormant conduction in more than one-third of patients with paroxysmal AF undergoing PVI. However, adenosine administration, and additional ablation of the resultant connections, does not improve long-term outcomes compared to a protocol that includes isoproterenol infusion.

Extraction of Respiratory Rate from Impedance Signal Measured on Arm: A Portable Respiratory Rate Measurement Device

In this paper, respiratory rate is extracted using signal processing and machine learning methods from electrical impedance, measured across arm. Two pairs of electrodes have been used along the arm, one for injecting the current, and one for sensing the voltage. After filtering, the frequency components and other signal features have been extracted using Short Time Fourier Transform (STFT). Then aSupport Vector Machine(SVM) model is trained to detect the breath-holding state. Frequency components and signal features of the parts of the signal that are detected to be representing the breathing state are then fed into another SVM model that extracts the respiratory rate and reduces the effect of motion artifacts. A similar method has been applied to the signal taken from end-tidal CO2 respiratory measurement device as the reference signal. This signal has been used as the ground truth for training of the SVM model and for validation of the method. The results are validated using 5-fold cross-validation method. Statistical analysis confirms the significance of the introduced features.

Impedance plethysmography on the arms: Respiration monitoring

A new method for extracting respiratory rate from electrical impedance measured on the arms is presented. The method requires application of only four electrodes to the subjects arms and is suitable to be used in a portable respiratory rate monitor and decision making systems. Set-Membership filtering is used to reduce the effect of motion artifact on the signal.

Multi-modal integrated approach towards reducing false arrhythmia alarms during continuous patient monitoring: The Physionet Challenge 2015

This work presents a solution for the Physionet Challenge 2015 regarding false alarm reduction in ICU. False alarms can result in alarm fatigue, i.e. reduced responsiveness of the ICU personnel to the true alarms due to an enormous number of false alarms. As a result, it is necessary to effectively suppress the false alarms while ensuring that the true alarms are not ignored. The challenge data contains five different types of alarms which are treated as independent problems in this paper. A separate subroutine is used for each alarm which is composed of two stages, peak detection and alarm verification. This paper uses a multi-modal peak detection algorithm that uses the information from all the available signals and combines the results from several peak detection algorithms to create a robust peak detection algorithm. The alarm verification stage is alarm dependent, composed of simple decision criteria or a complicated neural network model. The proposed approach achieves an overall score of 74.48 for the real-time event, where only the portions of the signals prior to the alarm are utilized, and 76.57 for the retrospective event, where 30 seconds of the signals after the alarm are used as well.

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician’s disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

Suppression of false arrhythmia alarms in the ICU: a machine learning approach

This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors. The outputs of the beat detection algorithms are combined using a machine learning approach. For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.

Epsilon-Tube Filtering: Reduction of High-Amplitude Motion Artifacts From Impedance Plethysmography Signal

The impedance plethysmography (IP) has long been used to monitor respiration. The IP signal is also suitable for portable monitoring of respiration due to its simplicity. However, this signal is very susceptible to motion artifact (MA). As a result, MA reduction is an indispensable part of portable acquisition of the IP signal. Often, the amplitude of the MA is much larger than the amplitude of the respiratory component in the IP signal. This study proposes a novel filtering method to remove the high-amplitude MAs from the IP signal. The proposed method combines the idea of ε-tube loss function and an autoregressive exogenous model to estimate the MA while leaving the periodic respiratory component of the IP signal intact. Also, a regularization method is used to find the best filter coefficients that maximize the regularity of the output signal. The results indicate that the proposed method can effectively remove the MA, outperforming the popular MA reduction methods. Several different performance measures are used for the comparison and the differences are found to be statistically significant.

ε-tube regression: A new method for motion artifact reduction

This paper introduces a new regression method, called ε-tube regression (ε-TR), for motion artifact reduction in physiological signals. It forms a tube arround the data which leads to an approximation that models only the motion artifact and not the target signal. Moreover, ε-TR prescribes the shape of the approximation using the available information about the motion artifact. The results show that ε-TR can effectively remove the motion artifacts from the impedance signal measured on the arms.

Reduction of periodic motion artifacts from impedance plethysmography

Motion artifact reduction is a fundamental part in portable monitoring of physiological signals. Here, the performance of three different motion artifact reduction methods, independent component analysis, least mean square filters and normalized least mean square filters are compared when applied to the impedance plethysmography signal. The results show that the performance of each method depends on the type of motion artifact it is being applied to, and that none of the methods universally performs better than the other ones. As a result, authors suggest that the motion artifact reduction algorithm should be designed based on the nature of the motion artifact.

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors—information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.