Time constant and filters

Biosignal Filters
The process of anaylsing and processing biosignals is a field of crucial importance concerning medical literature and practice. Information embedded in biosignals are challenging to observe from the raw data obtained. It has propelled biomedical engineers to develop and apply certain techniques such as the time constants and relative filters.

Signals may be interfered with by noise distortions or artifacts which may originate at the skin electrode interface, unstable dc offset or muscle noise. Fortunately, filters attempt at removal of these unwanted noises while allowing passage of certain signal frequencies through.

The time constant, represented by the Greek letter τ (tau), is a specific parameter which characterises the speed taken to respond to a step input of a first order, linear time invariant. It is usually equivalent to the time taken for a specified parameter to vary by a factor of 1− 1/ e (approx. 0.6321).

Importance in Clinical Medicine
Filters are involved with non-invasive medical diagnostic testing utilized to identify and diagnose diseases affecting the heart. It is also involved in the scope of Electrophysiology and Kinesiology. Electrocardiography (ECG), electroencephalography (EEG) electromyography (EMG) contain filters which highlight crucial aspects of cardiovascular systems by removing unwanted noise and distortion. Filters and time constants are focal in retrieving efficient information, helping to achieve higher quality assurance of detection, prevention for early or onset stages of heart abnormalities and avoid erroneous readings of the signal. Filters and time constants are involved in a systematic process to monitor and evaluate information. It is important for diagnostic purposes to remove noise since the signal should be accurately interpreted for further analysis and development. These tools are also used in intensive care, ambulatory etc.

Types of Filters

 * Low pass filter

Low-pass devices filter signals of a lower frequency than the limit allowed and limit higher frequency signals than the limit allowed. The combination of resistance and capacitance gives the time constant of the filter. The break frequency, also called the turnover frequency or cutoff frequency (in hertz), is determined by the time constant:

Advantages

The LowPassFilter is good for removing a small amount of high frequency noise from an N dimensional signal. You can either smooth an N dimensional signal by setting the filterFactor of the filter to a low value (i.e. a filterFactor value of 0.1 will result in a large amount of smoothing), alternatively, if you know the exact frequency of the noise you want to remove from the signal then you can explicitly set the cutoff frequency of the filter (using the setCutoffFrequency(...) function).

Disadvantages

Given that this filter is only a first-order filter, it may not give you a step enough cutoff frequency for the application you need. If this is the case then you may want to try a Moving Average Filter or Double Moving Average Filter instead.

A high-pass filter is an electronic filter that passes signals with a frequency higher than a certain cutoff frequency and attenuates signals with frequencies lower than the cutoff frequency. The amount of attenuation for each frequency depends on the filter design. A high-pass filter is usually modeled as a linear time-invariant system. The product of the resistance and capacitance (R×C) is the time constant (τ); it is inversely proportional to the cutoff frequency fc, that is,
 * High pass filter

Advantages

for long term periodic waveforms the main effect is to shift or keep the waveform around the centerline, known as the "baseline" in ECG.Most test engineers have little problem to understand this side of high pass filters.

Disadvantages

short term pulses, the effects of high pass filters on waveforms are not so easy to visualise. In particular, it is possible to get negative voltages out of a positive pulse waveform, and also peak to peak values exceeding the input. These effects cannot occur with a low pass filter.

Function of Time constant
In the time domain, the usual choice to explore the time response is through the step response to a step input, The time constant is also used to characterize the frequency response of various signal processing systems such as digital filters – which can be modeled or approximated by first-order LTI systems. A signal can be analysed and processed in two domains, time and frequency which is typically seen in ECGs.

Future Developments
Conclusively, a balance has to be found between removing noise and preserving the original signal to avoid disparities in diagnosis and ease the acquiring of information. In clinical environments,  the balance may be easily influenced so filters must be adjusted to get the best balance possible. Signal filtering should be considered when applying the amplitude and time diagnostic criteria.

Books
Béla G. Lipták (2003). Instrument Engineers' Handbook: Process control and optimization (4 ed.). CRC Press. p. 100. ISBN 0-8493-1081-4.

E. Kaniusas (2012); Biomedical Signals and Sensors I; Biological and Medical Physics; Biomedical Engineering; p.298; ISBN 978-3-642-24842-9

Article Journals
Kasar,S, Mishra, A & Joshi, M (2014); Performance of Digital filters for noise removal from ECG signals in Time domain; INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS, INSTRUMENTATION AND CONTROL ENGINEERING; 2321 – 5526; Vol. 2, Issue 4; ISSN (Online) 2321.

C. J. D'Luca et al ( 2010);Filtering the surface EMG signal: Movement artifact and baseline noise contamination;Journal of Biomechanics 43; 1573–1579; doi:10.1016/j.jbiomech.2010.01.027

Webpages
Peter Selvey, MEDTEQ; http://www.medteq.info/med/

Webpage: name of at least one author or organization; url.