Discrete Wavelet Transform as a Tool for Data Analysis

As medical devices are increasingly required to provide for information from the point of care to enterprise electronic health record systems, more patient data collected at the point of care will become available for remote viewing and analysis. In this paper a discrete wavelet transform method is presented for automatically filtering and analyzing data obtained from high acuity bedside physiologic monitors using discrete wavelet transforms, the purpose being to reduce the total amount of data transmitted into the electronic health record, and to facilitate analysis of time-based trends in physiologic data.

Use of Discrete Wavelet Transform Compression

As information technology has been brought into the healthcare enterprise, much of the paper-based record is being supplanted by an electronic record, in which clinicians either record information manually or, in addition, automatically from clinical systems. The electronic medical record is maintained by the health care enterprise and follows the patient throughout all phases of diagnosis and treatment. Furthermore, this medical record is accessible to all authorized clinical personnel. An obvious benefit of this approach is that, unlike the paper record, the electronic medical record can be accessed from many different locations without physically retrieving the patient’s hard copy information from particular departments. Loss of information is nullified, and use of the electronic medical record establishes a standard approach for recording of patient information, so that each department must conform to specific standards in terms of the types and quality of information recorded on each patient. Also, with Web-browser-based medical record viewing, convenience in terms of viewing, together with the reduced delays associated with retrieving the paper recording ensure that clinicians can readily obtain patient information when required. In addition, two-way communication between the enterprise information system and clinical systems enable the error-free retrieval of patient demographic and administrative information (such as medical record number and insurance information) without adding further delay or introducing errors into the patient’s record within the departmental system.

One key difference between the legacy paper record and the electronic medical record is that the paper record remained an intimate device by which the attending nurse monitored and recorded status on the patient: it remained with the patient and the nurse until the patient left the unit. With the introduction of the electronic medical record, data transmitted to that record and viewable by authorized individuals outside of the unit can lack the context of the actual situation in interpreting patient flow sheet results. As society and healthcare move toward a completely automated and electronic medical record environment, it must be mindful of the fact that the introduction of new technologies must never impede quality healthcare [1].

In this paper the author describes a process for augmenting the basic transmission of information from the clinical environment to the medical record by capturing more detailed information that may not be captured during the course of standard flow sheet recording. Patient telemetry is normally recorded within the flow or assessment sheet on regular intervals. Typical intervals range from 15 minutes to an hour, depending on the particular acuity of the patient. However, bedside monitors typically can record very detailed information in fractions of a second. Most of this information is discarded, and much of it can be of no clinical value at these short intervals. However, a trade-off exists in terms of the size of the interval and the capturing of relatively important data from these bedside monitors: make the recording interval too large, and events of relatively short duration but high importance (such as heart rate spikes, or respiratory rate increases) will be missed and never recorded within the electronic medical record. On the other hand, make these cording intervals too small, and the health care enterprise, including the hospital computing network and the size of the medical record, will become cumbersome and filled with much useless information, possibly even rendering the system unusable. One approach to solving this problem is to provide the capability to record detailed information when necessary, but omit when not.

Use of Discrete Wavelet Transform as a Method for Data Compression

Performance and response time is a key metric when communicating raw clinical data from departmental to electronic health systems within a healthcare enterprise.  The objective of this paper was to present a discrete wavelet transform method that can reduce the overall data storage requirements as well as facilitate data analysis of time-varying signals without requiring large scale data mining of the raw information. The discrete wavelet transform (DWT) was selected as a possible filtering mechanism because the DWT preserves both spatial and temporal behavior of a raw data signal. This is a very important feature in the study of medical telemetry, because many processes are not stationary, making the application of traditional signal processing methods (such as Fourier transforms) inappropriate. The creation of a DWT Processing method that exists as an adjunct to the existing departmental information system imposes no additional software features on the existing telemetry system, and operates off of the existing clinical network. Furthermore, the benefits of using the DWT Processor as both a noise filter and as an automatic data filter are affirmed inasmuch as both stationary and non-stationary signals are processed appropriately using the DWT method: stationary signals can be represented by relatively few overall data points in the form of wavelet coefficients, whereas threshold filtering of non-stationary signal can provide accurate reconstruction of raw signals with even a factor of two fewer data points than the original signal. This benefits a potentially congested network and speeds recreation of the original signal by requiring fewer overall calculations to be performed by that enterprise information system.

A copy of the PDF version of the paper is included here:

Discrete Wavelet Transform.

[1]  Patricia Benner, “Beware of Technological Imperatives and Commercial Interests That Prevent Best Practices,” American Journal of Critical Care; 12(5): 469-471, 2003.