Tomás Teijeiro

An open platform for the protocolization of home medical supervision

This paper describes SERVANDO, a distributed open platform that deals with a series of recurrent problems in current telemedicine systems, particularly: (1) the scheduling of the different medical actions that should be executed, organized in a personalized agenda generated from a follow-up protocol; (2) functionality encapsulation and reuse in a set of services; (3) communications between the home of the patient and the hospital, through a flexible scheme for bidirectional message exchange; or (4) the management of the events generated during the monitoring. Supervision of patients is carried out through last generation smartphones. SERVANDO provides comprehensive facilities for generic telemedicine applications development, adaptable according to the disease and the particular characteristics of the patient. At the moment, with validation purposes, a follow-up protocol for the supervision of patients with chronic obstructive pulmonary disease has been implemented.

Heart rate variability in patients with severe chronic obstructive pulmonary disease in a home care program

BACKGROUND Chronic obstructive pulmonary disease (COPD) patients present functional and structural changes of the respiratory system that have a profound influence on cardiac autonomic dysfunction. OBJETIVE To analyse heart rate variability in COPD patients under stable condition and during acute exacerbation episodes (AECOPD). METHODS Twenty three severe COPD male patients, 69.6 ± 7.3 years, in stable condition were followed up for two years. Home visits were carried out by a nurse every month, and home or hospital visits were arranged on demand. Every three months an ECG, oxygen saturation and spirometric recording was obtained for each patient. If the patient presented AECOPD compatible clinical data the same measurements were performed before any change of treatment. Spectral parameters of heart rate variability in time and frequency domains were obtained from ECG. The time evolution of power in low frequency (LF) and high frequency (HF) bands were obtained from the spectrogram. In addition, we calculated the LF/HF ratio and total heart rate variability power (POW). RESULTS We analysed 154 patient-visit records during the follow up, pertaining to 23 patients and 8 controls; 19 of the patients had experienced at least one AECOPD. Stable COPD patients had higher HF values than control subjects. No significant differences were found in LF, LF/HF ratio or POW variables. AECOPD patients had higher LF, HF and POW than the stable COPD and control groups. CONCLUSION AECOPD patients exhibited signs of increased autonomic activity compared with stable COPD.

Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG

Objective: This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. Methods: A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation. Finally, a rule-based classifier assigns a tag to each cluster. Results: The method has been tested with the MIT-BIH Arrhythmia Database records, showing a significantly better performance than any other automatic approach in the state-of-the-art, and even improving most of the assisted approaches that require the intervention of an expert in the process. Conclusion: The most relevant issues in ECG classification, related to a large extent to the variability of the signal patterns between different subjects and even in the same subject over time, will be overcome by changing the reasoning paradigm. Significance: This paper demonstrates the power of an abductive framework for time-series interpretation to make a qualitative leap in the significance of the information extracted from the ECG by automatic methods.

A PDA-based modular and multipurpose system for intelligent ubiquitous monitoring

This paper proposes a ubiquitous patient-monitoring system using mobile devices, in which all communications are based on the use of wireless technology. The most salient features of the system are: 1) its multi-purpose nature — the architecture has been conceived so that the device can be configured to monitor patients with different illnesses; 2) the organisation of the physiological signal processing through a layout of processing levels structured into modules which can be incorporated and substituted in a simple manner, facilitating the reconfiguration of the monitoring functions; and 3) the use of techniques from the field of Artificial Intelligence to provide information closer to the decision-making process of medical specialists.

SERVANDO: An extensible platform for home-care services providing

This paper presents an extensible distributed platform that aims to speed up the development of personalized telemedicine systems, dealing with a series of recurrent problems in this kind of system, particularly: (1) functionality encapsulation and reuse in a set of services; (2) communications between the patients home and the hospital, through a flexible scheme for bidirectional message exchange; and (3) the interaction between patients and the system. Home supervision is carried out through last generation smartphones. To date, the platform has been used for the follow-up of patients with COPD and cardiovascular diseases.

On the adoption of abductive reasoning for time series interpretation

Abstract Time series interpretation aims to provide an explanation of what is observed in terms of its underlying processes. The present work is based on the assumption that the common classification-based approaches to time series interpretation suffer from a set of inherent weaknesses, whose ultimate cause lies in the monotonic nature of the deductive reasoning paradigm. In this document we propose a new approach to this problem, based on the initial hypothesis that abductive reasoning properly accounts for the human ability to identify and characterize the patterns appearing in a time series. The result of this interpretation is a set of conjectures in the form of observations, organized into an abstraction hierarchy and explaining what has been observed. A knowledge-based framework and a set of algorithms for the interpretation task are provided, implementing a hypothesize-and-test cycle guided by an attentional mechanism. As a representative application domain, interpretation of the electrocardiogram allows us to highlight the strengths of the proposed approach in comparison with traditional classification-based approaches.

Using Temporal Abduction for Biosignal Interpretation: A Case Study on QRS Detection

In this work, we propose an abductive framework for bio signal interpretation, based on the concept of Temporal Abstraction Patterns. A temporal abstraction pattern defines an abstraction relation between an observation hypothesis and a set of observations constituting its evidence support. New observations are generated abductively from any subset of the evidence of a pattern, building an abstraction hierarchy of observations in which higher levels contain those observations with greater interpretative value of the physiological processes underlying a given signal. Non-monotonic reasoning techniques have been applied to this model in order to find the best interpretation of a set of initial observations, permitting even to correct these observations by removing, adding or modifying them in order to make them consistent with the available domain knowledge. Some preliminary experiments have been conducted to apply this framework to a well known and bounded problem: the QRS detection on ECG signals. The objective is not to provide a new better QRS detector, but to test the validity of an abductive paradigm. These experiments show that a knowledge base comprising just a few very simple rhythm abstraction patterns can enhance the results of a state of the art algorithm by significantly improving its detection F1-score, besides proving the ability of the abductive framework to correct both sensitivity and specificity failures.

Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification.

OBJECTIVE This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single-lead ECG signals, emphasizing the importance of the interpretability of the classification results. APPROACH A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the Construe abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions can be used to elucidate the expert criteria underlying the labeling of the 2017 PhysioNet/CinC Challenge dataset, enabling a manual partial relabeling to improve the consistency of the training set. Finally, a tree gradient boosting model and a recurrent neural network are combined using the stacking technique to provide an answer on the basis of the feature values. MAIN RESULTS The proposal was independently validated against the hidden dataset of the Challenge, achieving a combined F 1 score of 0.83 and tying for the first place in the official stage of the Challenge. This result was even improved in the follow-up stage to 0.85 with a significant simplification of the model, attaining the highest score so far reported on the hidden dataset. SIGNIFICANCE The obtained results demonstrate the potential of Construe to provide robust and valuable descriptions of temporal data, even with the presence of significant amounts of noise. Furthermore, the importance of consistent classification criteria in manually labeled training datasets is emphasized, and the fundamental advantages of knowledge-based approaches to formalize and validate those criteria are discussed.

A noise robust QRS delineation method based on path simplification

In this work we present a new method for the delineation of QRS complexes in ECG signals. The main objective is to provide a robust method that gives acceptable results even under severe noise conditions in the input signal, as often happens in continuous bedside or home monitoring. Our method is based on relevant point selection using a path simplification algorithm, and then a clustering strategy combined with a qualitative description of the waveform is applied in order to select the most promising signal segments to include inside the QRS limits. The validation was performed by adding different levels of noise to the records of a standard database, and then comparing our proposal with a state-of-the art approach. Results show a high sensitivity and stable error evolution even at the highest noise levels.