Epilepsy affects approximately 1% of the global population and is one of the most common neurological disorders worldwide. Using electroencephalogram (EEG) data, deep learning (DL) has been proposed to automatically detect seizures, assisting clinicians in diagnosing and managing seizures in patients. However, EEG signals often present challenges such as variability in seizure patterns and artefacts. Other biosignals can be useful in seizure detection, yet most studies have primarily focused on EEG or single biosignals, largely ignoring the potential performance improvements offered by integrating multiple biosignals. In this work, we proposed a DL algorithm that utilises multi-biosignals, including EEG, electrocardiogram (ECG), electromyography (EMG), and respiratory signals. The algorithm employs multi-scaled window lengths with convolutional cross-attention on different biosignals for the final prediction of seizures. The cross-attention mechanism facilitates the interaction of information from short-term and long-term signals across multi-biosignals. Our study, evaluated on 63 patients, demonstrated an improvement in seizure detection, achieving an AUC of 0.90, an accuracy of 0.90, a specificity of 0.91, and a sensitivity of 0.70, compared to the EEG-only algorithm which achieved an AUC of 0.86 and sensitivity of 0.51. Notably, our algorithm outperformed an earlier study using the identical dataset by a margin of 0.25 in AUC. Furthermore, we conducted validation studies with other models, showing that our algorithm outperformed commonly used DL algorithms for seizure detection.