Time constant and filters
GOOD TRY FOR A DIFFICULT SUBJECT!
Biosignal Filters[✎ edit | edit source]
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[✎ edit | edit source]
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[✎ edit | edit source]
- Low Pass filter
Low-pass devices filter signals of a lower frequency than the limit allowed and block higher frequency signals than the limit allowed . The combination of resistance and capacitance gives the time constant of the filter :
It is effective in removal of small amounts of high frequencies in N Dimensional signals. By adjusting factors on the filter, you can set it to a low value, resulting in smoother sets of data obtained.
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.
- High Pass filter
A high-pass filter allows higher frequency signals to pass and diminishes frequencies of a lower nature of the limit allowed. is an electronic filter The amount of attenuation for each frequency depends on the filter design. A high-pass filter utilises the linear invariant system. The time constant is inversely proportional to the cutoff frequency :
Waveforms over a long period of time are shifted to keep the waveform stable and centred, known as the baseline in ECG. This is an easy component of the high pass filter and therefore medical bioengineers do not have any difficulties attaining information.
High pass filters cannot easily depict and represent waveforms over a short term pulses. Specifically, it is possible to observe negative voltages produced by positive pulse waveforms as opposed to absence of these effects using a low pass filter.
- Band-Pass Filter
Band pass filters are designed to hone in frequencies from a broader range of mixed ones. It is composed of both properties of low-pass and high-pass filters.
This device works primarily on the concept that all frequencies are within a certain spectrum and therefore blocks out frequencies which deviate from this spectrum such as high or low frequencies.
In order to function appropriately, this filter relies on blockage of unneeded frequencies on either section. It is hard to design such filters which allow completely unhindered or undistorted frequencies through in a desired frequency range.
Function of Time constant[✎ edit | edit source]
Signals can be analysed more effectively if it is processed in the two domains of time and frequency, usually seen in ECGs. A step response to the step input is used in the exploration of the time response. Various processing systems that measure signals such as digital filters use time constants to aid in characterising frequency responses to these filters. These responses are used to appropriate which models and methods can be applied to first order LTI systems.
Future Developments[✎ edit | edit source]
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.
References[✎ edit | edit source]
Books[✎ edit | edit source]
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[✎ edit | edit source]
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[✎ edit | edit source]
Peter Selvey, MEDTEQ; http://www.medteq.info/med/