Valeria Villani

Baseline wander removal for bioelectrical signals by quadratic variation reduction

Baseline wander is a low-frequency additive noise affecting almost all bioelectrical signals, in particular the ECG. In this paper, we propose a novel approach to baseline wander estimation and removal for bioelectrical signals, based on the notion of quadratic variation reduction. The quadratic variation is meant as a measure of variability for vectors or sampled functions, and is a consistent measure in this regard. Baseline wander is estimated solving a constrained convex optimization problem where quadratic variation enters as a constraint. The solution depends on a single parameter whose value is not critical, as proven by a sensitivity analysis. Numerical results confirm the effectiveness of the approach, which outperforms state-of-the-art algorithms. The algorithm compares favorably also in terms of computational complexity, which is linear in the size of the vector to detrend. This makes it suitable for real-time applications as well as for applications on devices with reduced computing power, such as handheld devices.

Baseline wander estimation and removal by quadratic variation reduction

The baseline wander is a low frequency additive noise partially overlapping the band of ECG signal. This makes its removal difficult without affecting the ECG. In this work we propose a novel approach to baseline wander estimation and removal based on the notion of quadratic variation. The quadratic variation is a suitable index of variability for vectors and sampled functions. We derive an algorithm for baseline estimation solving a constrained convex optimization problem. The computational complexity of the algorithm is linear in the size of the ECG record to detrend, making it suitable for realtime applications. Simulation results confirm the effectiveness of the approach and highlight its ability to remove baseline wander. Eventually, the proposed algorithm is not limited to ECG signals, but can be effectively applied whenever baseline estimation and removal are needed, such as EEG records.

Fast ECG baseline wander removal preserving the ST segment

Baseline wander removal is an unavoidable preprocessing step in ECG signal analysis. Unfortunately, the in-band nature of this kind of noise makes its removal difficult without affecting ECG waveform, in particular the ST segment. This is a portion of the ECG with high clinical relevance, as it is related to the diagnosis of acute coronary syndromes. The ST segment is highly susceptible to distortion when baseline removal is performed affecting the low-frequency region of ECG spectrum, where are concentrated the harmonic components that mainly contribute to the shape of the ST segment. In this paper, we propose to tackle the problem of baseline removal from a different perspective, considering the quadratic variation as an alternative measure of variability not directly related to the frequency domain. In this regard, we recently proposed a novel baseline removal algorithm based on quadratic variation reduction. In this paper, we assess its performance with respect to the distortion of the ST segment comparing it to state-of-the-art algorithms. Simulation results confirm the effectiveness of the approach based on quadratic variation reduction. Our algorithm outperforms state-of-the-art algorithms tailored to minimize distortion of the ST segment. Moreover, it compares favorably also in terms of computational complexity, which is linear in the size of the vector to detrend. This makes it suitable also for real-time applications.

A Natural Infrastructure-Less Human–Robot Interaction System

This letter introduces a novel methodology for letting a user interact with a robotic system in a natural manner. The main idea is that of defining an interaction system that does not require any specific infrastructure or device but relies on commonly utilized objects while leaving the users hands free. Specifically, we propose to utilize a smartwatch (or any commercial sensorized wristband) for recognizing the motion of the users forearm. This is achieved by measuring accelerations and angular velocities, which are then elaborated for recognizing gestures and for defining velocity commands for the robot. The proposed interaction system is evaluated experimentally with different users controlling a semiautonomous quadrotor. Results show that the usability and effectiveness of the proposed natural interaction system provide significant improvement in the human–robot interaction experience.

Denoising and harmonic artifacts rejection for ECG P-waves by quadratic variation reduction

Atrial fibrillation (AF) is a common cardiac arrhythmia related to irregular atrial contractions. Several studies have shown that the analysis of P-waves extracted from ECG signals is helpful in understanding the predisposing factors to AF. However, P-waves are usually highly corrupted by noise and harmonic artifacts and this makes quite difficult their analysis. Recently we proposed a novel algorithm for denoising P-waves based on the notion of quadratic variation reduction. It is quite good in denoising P-waves affected by noise, but its effectiveness reduces when it is used in filtering out harmonic artifacts, like power-line interference. In this paper we propose an algorithm that overcomes this limitation and extends our previous method allowing it to both denoise and reject harmonic artifacts. Simulation results confirm the effectiveness of the approach and highlight its ability to remove both noise and artifacts. The algorithm has reduced computational complexity and this makes it suitable for real-time applications.

ECG baseline wander removal by QVR preserving the ST segment

Baseline wander removal is an unavoidable step in ECG signal processing. The in-band nature of this noise makes its removal difficult without affecting the ECG, in particular the ST segment. This portion of the ECG has high clinical relevance, as it is related to the diagnosis of acute coronary syndromes. We have recently proposed a novel approach to baseline wander removal based on the notion of quadratic variation reduction. In this paper, we assess its performance in terms of both effectiveness in removing baseline wander and distortion introduced in the ST segment. Numerical results highlight the effectiveness of the approach, which outperforms state-of-the-art algorithms both in removing baseline drift and preserving the ST segment. The algorithm is also very fast, as its computational complexity is linear in the size of the vector to detrend.

ECG smoothing and denoising by local quadratic variation reduction

The ECG is the standard noninvasive test used to measure the electrical activity of the heart. Unfortunately, ECG signal is corrupted by several kinds of noise and artifacts that may negatively affect any subsequent analysis. In this work, we present a fast and effective algorithm for smoothing and denoising ECG records. The algorithm is the closed-form solution to a constrained convex optimization problem, where smoothing and denoising are achieved by locally reducing the quadratic variation of different portions of the ECG. Such a reduction is inversely related to the local SNR. The computational complexity of the algorithm is linear in the size of the vector under analysis, thus making it suitable for real-time applications. Simulation results confirm the effectiveness of the approach and highlight a notable ability to smooth and denoise ECG signals.

Interacting With a Mobile Robot with a Natural Infrastructure-Less Interface

Abstract In this paper we introduce a novel approach that enables users to interact with a mobile robot in a natural manner. The proposed interaction system does not require any specific infrastructure or device, but relies on commonly utilized objects while leaving the user’s hands free. Specifically, we propose to utilize a smartwatch (or a sensorized wristband) for recognizing the motion of the user’s forearm. Measurements of accelerations and angular velocities are exploited to recognize user’s gestures and define velocity commands for the robot. The proposed interaction system is evaluated experimentally with different users controlling a mobile robot and compared to the use of a remote control device for the teleoperation of robots. Results show that the usability and effectiveness of the proposed natural interaction system based on the use of a smartwatch provide significant improvement in the human-robot interaction experience.

A detection-estimation approach to filtering with intermittent observations with generally correlated packet dropouts

This paper is concerned with the problem of state estimation for the class of linear discrete-time Gaussian systems with intermittent observations due to packet losses. This is a common case in networked control systems, where the state of a remote plant is estimated from measurements carried through a lossy network. We assume that the receiver does not know the sequence of packet dropouts. This is typical, e.g., in wireless sensor networks or in networks that cannot rely on protocols that provide information on packet loss. Moreover, we assume that the sequence of packet dropouts is correlated, thus subsuming both the cases of independent dropouts and dropouts modeled as a Markov chain. We propose a detection-estimation approach to the problem of state estimation. The estimator consists of two stages: the first is a nonlinear optimal detector, which decides if a packet dropout has occurred, and the second is a time-varying Kalman filter, which is fed with both the observations and the decisions from the first stage. The overall estimator has finite memory and the tradeoff between performance and computational complexity can be easily controlled. As a case study, we derive the decision rule in closed form in the case of dropout sequence modeled as a Markov chain. Simulation results highlight the effectiveness of the proposed approach, which outperforms the linear recursive estimator of Hadidi and Schwartz.

A General Approach to Natural Human-Robot Interaction

This paper proposes a scheme for letting a human interact with a generic robot in a natural manner. Based on the concept of natural user interfaces, the proposed methods exploit recognition of the users’ forearm motion to produce commands for the robotic system. High-level commands are provided based on gesture recognition, and velocity commands are computed for the robot by mapping, in a natural manner, the motion of the user’s forearm. The method is proposed in a general manner, and is then instantiated considering two different robotic systems, namely a quadrotor and a wheeled mobile robot. Usability of the system is evaluated with experiments involving users.