Breath detection algorithm in digital computers

Optimized breath detection algorithm in electrical impedance tomography

OBJECTIVE

This paper defines a method for optimizing the breath delineation algorithms used in electrical impedance tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern.

APPROACH

Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project.

MAIN RESULTS

Three different algorithms are proposed that improve the breath detector performance by adding conditions on (1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, (2) minimum tidal impedance amplitude and (3) minimum tidal breath rate obtained from time-frequency analysis. As a baseline a zero-crossing algorithm has been used with some default parameters based on the Swisstom EIT device.

SIGNIFICANCE

Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the receiver operating characterics. This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.

Novel approach to computerized breath detection in lung function diagnostics

BACKGROUND

Breath detection, i.e. its precise delineation in time is a crucial step in lung function data analysis as obtaining any clinically relevant index is based on the proper localization of breath ends. Current threshold or smoothing algorithms suffer from severe inaccuracy in cases of suboptimal data quality. Especially in infants, the precise analysis is of utmost importance. The key objective of our work is to design an algorithm for accurate breath detection in severely distorted data.

METHODS

Flow and gas concentration data from multiple breath washout test were the input information. Based on universal physiological characteristics of the respiratory tract we designed an algorithm for breath detection. Its accuracy was tested on severely distorted data from 19 patients with different types of breathing disorders. Its performance was compared to the performance of currently used algorithms and to the breath counts estimated by human experts.

RESULTS

The novel algorithm outperformed the threshold algorithms with respect to their accuracy and had similar performance to human experts. It proved to be a highly robust and efficient approach in severely distorted data. This was demonstrated on patients with different pulmonary disorders.

CONCLUSION

Our newly proposed algorithm is highly robust and universal. It works accurately even on severely distorted data, where the other tested algorithms failed. It does not require any pre-set thresholds or other patient-specific inputs. Consequently, it may be used with a broad spectrum of patients. It has the potential to replace current approaches to the breath detection in pulmonary function diagnostics.

Basic principles of respiratory function monitoring in ventilated newborns: A review

Respiratory monitoring during mechanical ventilation provides a real-time picture of patient-ventilator interaction and is a prerequisite for lung-protective ventilation. Nowadays, measurements of airflow, tidal volume and applied pressures are standard in neonatal ventilators. The measurement of lung volume during mechanical ventilation by tracer gas washout techniques is still under development. The clinical use of capnography, although well established in adults, has not been embraced by neonatologists because of technical and methodological problems in very small infants. While the ventilatory parameters are well defined, the calculation of other physiological parameters are based upon specific assumptions which are difficult to verify. Incomplete knowledge of the theoretical background of these calculations and their limitations can lead to incorrect interpretations with clinical consequences. Therefore, the aim of this review was to describe the basic principles and the underlying assumptions of currently used methods for respiratory function monitoring in ventilated newborns and to highlight methodological limitations.

Comparative investigations of algorithms for the detection of breaths in newborns with disturbed respiratory signals

The correct detection of the beginning of inspiration and expiration in the respiratory signals is an essential prerequisite for accurate lung function testing in newborns. Five algorithms for breath detection using pneumotachographically measured flow and volume signals were investigated with regard to the error rate. To compare and to evaluate the reliability of these algorithms 12 minimally and 12 severely disturbed flow and volume signals from spontaneously breathing newborns were used. With the exception of an algorithm based on Walsh-transformed signals, all algorithms work reliably (error rate <1.1%) if disturbances are minimal. In severely disturbed signals there is a great difference between the algorithms. The most robust algorithm tested (trigger of the flow signal with an additional plausibility check of the recognized breath) resulted in an error rate of <3.4%. Not all algorithms tested are suitable for real-time applications because they differ considerably in delay time for breath detection.

Computerized automatic system to obtain inspiration/expiration chest roentgenograms without patient collaboration

We have developed a computerized automatic X-ray image acquisition system. It consists of conventional pressure and flow transducers connected to a commercial data acquisition card installed in a PC/AT, the card output being connected through a relay to a conventional X-ray system. Signal processing, that has been applied for automatic detection of inspiration and expiration in respiratory signals, allows the X-ray equipment activation at a given moment of the respiratory cycle. Radiographs are thus obtained either in inspiration or expiration at tidal volume on anaestesized experimental animals. The system has also been tested on non-collaborating patients.

Challenges and opportunities for computerizing the anesthesia record

Most observers would agree that the goal of computerizing the anesthesia record is a worthy one. Despite the fact that several academic groups and vendors have attempted to develop and provide computerized anesthesia charting, the practice is not widespread. In this review article, we attempt to outline the reasons for this reluctance to use computers for anesthesia charting. Where there are problems to be solved, there also are opportunities. We discuss the development of strategies to solve these problems and thus present opportunities for medical informatics professionals and anesthesiologists to work toward joint solutions. Solving these problems includes the development of consensus standards and working out technical, social, and educational difficulties. Details of the approaches recommended are outlined.