*3.2. Electrical Detection*

The use of electrical circuits to gather data in the form of electrical signals is known as electrical detection. These signals can take the form of impedance, voltage, current, or any other electrical signal [41]. Among these, impedance is the most commonly used parameter for identifying and quantifying cells. As a cell or particle passes through the electrodes in a microfluidic channel, it causes a change in impedance, and the output signal is determined by the cell's properties, such as size, conductivity, and permittivity. Compared to traditional optical detection, the electrical detection of cells has several advantages, including a smaller footprint and lower cost due to the absence of bulky optical equipment [41]. In the following paragraphs, we will discuss the biosensors that utilize machine learning techniques for the electrical detection of various cancer cells. A schematic diagram of an electrical impedance cytometer with ANN for data analysis is shown in Figure 7.

**Figure 7.** Schematic diagram of an electrical impedance cytometer. As cells flow through microfluidic chips, the change in impedance is measured by a lock-in amplifier. The lock-in amplifier can apply signals in different frequencies at a time. The data is then recorded and analyzed using the ANN algorithm.

Within the preceding section, we provided an overview of the optical biosensors, which, in conjunction with machine learning, are utilized to analyze the data acquired from the sensors. Additionally, we emphasized the utility of an affordable and non-invasive biosensor in the detection of cancer cells. This section will focus on the employment of electrical-based biosensors in combination with machine learning for the detection of cancer.
